WO2020147594A1 - Procédé, système et dispositif pour obtenir une expression de relation entre des entités, et système de récupération de publicité - Google Patents

Procédé, système et dispositif pour obtenir une expression de relation entre des entités, et système de récupération de publicité Download PDF

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WO2020147594A1
WO2020147594A1 PCT/CN2020/070249 CN2020070249W WO2020147594A1 WO 2020147594 A1 WO2020147594 A1 WO 2020147594A1 CN 2020070249 W CN2020070249 W CN 2020070249W WO 2020147594 A1 WO2020147594 A1 WO 2020147594A1
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heterogeneous
node
nodes
graph
sample data
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陈怡然
温世阳
吴文金
林伟
朱晓宇
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Alibaba Group Holding Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0623—Electronic shopping [e-shopping] by investigating goods or services
    • G06Q30/0625—Electronic shopping [e-shopping] by investigating goods or services by formulating product or service queries, e.g. using keywords or predefined options

Definitions

  • the present invention relates to the technical field of data mining, in particular to a method, system and equipment for obtaining expressions of relationships between entities, and an advertisement recall system.
  • the inventor of the present invention found:
  • a graph is composed of nodes and edges.
  • a node is used to represent an entity, and the edge between nodes is used to represent the relationship between nodes.
  • a graph generally includes more than two nodes and more than one edge. Therefore, a graph can also be understood as consisting of a collection of nodes and a collection of edges, usually expressed as: G(V, E), where G represents the graph , V represents the set of nodes in the graph G, and E is the set of edges in the graph G.
  • G represents the graph
  • V represents the set of nodes in the graph G
  • E is the set of edges in the graph G.
  • Graphs can be divided into homogeneous graphs and heterogeneous graphs.
  • a heterogeneous graph refers to different types of nodes in a graph (the types of edges can be the same or different), or different types of edges in a graph (the types of nodes can be the same or different). Therefore, when there are many types of entities that need to be expressed by multiple types of nodes, or the relationship between entities does not need to be expressed by multiple types of edges, it is preferable to express these entities and their relationships through heterogeneous graphs.
  • the magnitude of the nodes and edges included in the heterogeneous graph is very large, the heterogeneous graph will be extremely complex and the amount of data will be very large. Therefore, reducing the complexity and data volume of the heterogeneous graph becomes the field Technical problems faced by technicians.
  • the present invention is proposed to provide a method, system and equipment for obtaining expressions of relationships between entities, and an advertisement recall system that overcomes or at least partially solves the above-mentioned problems.
  • the embodiment of the present invention provides an advertisement recall system, including a system for obtaining relationship expressions between entities and an advertisement recall matching system;
  • the system for obtaining expressions of relationships between entities is used to construct a heterogeneous graph for advertisement search scenarios, and the node types in the heterogeneous graph include: at least one of advertisements, commodities, and query words.
  • the types of edges include at least one of click edges, co-click edges, collaborative filtering edges, content semantically similar edges, and attribute similar edges;
  • the preset graph convolution model learns a batch of sample data according to heterogeneous subgraphs to obtain the vector expression of nodes in heterogeneous subgraphs.
  • a graph convolution model corresponds to a heterogeneous subgraph;
  • the preset aggregation model is based on sample data and aggregates the vector expressions of the same node in different heterogeneous subgraphs to obtain the same vector expression of the same node in different heterogeneous subgraphs;
  • the preset loss function optimizes the parameters of the model based on the same vector expression of the sample data and the same node;
  • a node of corresponds to an entity in the sample data
  • the advertisement recall matching system is configured to use the low-dimensional vector expressions of query term nodes, commodity nodes and search advertisement nodes obtained by the system for obtaining inter-entity relationship expressions to determine the relationship between query term nodes, commodity nodes and search advertisement nodes According to the matching degree, select search advertisements that match the product and query terms according to the set requirements.
  • a meta-path corresponds to a heterogeneous subgraph
  • the meta-path is used to express the structure of the heterogeneous subgraph and the node types and edge types included in the heterogeneous subgraph are specifically: a meta-path Used to express the structure of a heterogeneous subgraph and the types of nodes and edges included in the heterogeneous subgraph;
  • the splitting the heterogeneous graph into at least two heterogeneous subgraphs according to the preset meta-path specifically includes:
  • the system for obtaining expressions of relationships between entities uses a preset graph convolution model to learn the sample data according to heterogeneous subgraphs to obtain vector expressions of nodes in the heterogeneous subgraphs , Specifically including:
  • the preset graph convolution model obtains the information of the nodes in the heterogeneous graph according to the attribute information of each node in the heterogeneous subgraph and the structure information and attribute information of the at least first-order neighbor nodes of each node in the heterogeneous subgraph.
  • Vector expression
  • the system for obtaining expressions of relationships between entities aggregates vector expressions of the same node in different heterogeneous subgraphs based on sample data through a preset aggregation model to obtain different heterogeneous subgraphs
  • the same vector expression of the same node includes:
  • the preset aggregation model is based on the sample data, using attention mechanism aggregation learning, fully connected aggregation learning, or weighted average aggregation learning to aggregate the vector expressions of the same node in different heterogeneous subgraphs to obtain different heterogeneous subgraphs.
  • attention mechanism aggregation learning fully connected aggregation learning
  • weighted average aggregation learning to aggregate the vector expressions of the same node in different heterogeneous subgraphs to obtain different heterogeneous subgraphs.
  • the same vector representation of the same node in the graph is based on the sample data, using attention mechanism aggregation learning, fully connected aggregation learning, or weighted average aggregation learning to aggregate the vector expressions of the same node in different heterogeneous subgraphs to obtain different heterogeneous subgraphs.
  • the same vector representation of the same node in the graph is based on the sample data, using attention mechanism aggregation learning, fully connected aggregation learning, or weighte
  • the advertisement recall matching system determining the degree of matching among query term nodes, commodity nodes and search advertisement nodes includes:
  • the virtual request node is a virtual node constructed by the query term node and the commodity node pre-clicked by the user under the common query term;
  • the matching degree between the query term node, the product node and the search advertisement node is determined.
  • the advertisement recall matching system selects search advertisements that match the product and the query term according to the matching degree, including:
  • a search advertisement whose distance meets the set requirement is selected.
  • the embodiment of the present invention also provides a method for obtaining expressions of relationships between entities, including:
  • the preset graph convolution model learns a batch of sample data according to heterogeneous subgraphs to obtain the vector expression of nodes in heterogeneous subgraphs.
  • a graph convolution model corresponds to a heterogeneous subgraph;
  • the preset aggregation model is based on sample data and aggregates the vector expressions of the same node in different heterogeneous subgraphs to obtain the same vector expression of the same node in different heterogeneous subgraphs;
  • the preset loss function optimizes the parameters of the model based on the same vector expression of the sample data and the same node;
  • a node in the heterogeneous graph corresponds to the sample data Of an entity.
  • a meta-path corresponds to a heterogeneous subgraph
  • the meta-path is used to express the structure of the heterogeneous subgraph and the node types and edge types included in the heterogeneous subgraph are specifically: a meta-path Used to express the structure of a heterogeneous subgraph and the types of nodes and edges included in the heterogeneous subgraph;
  • the splitting the heterogeneous graph into at least two heterogeneous subgraphs according to the preset meta-path specifically includes:
  • the one meta-path is used to express the structure of a heterogeneous subgraph and the node types and edge types included in the heterogeneous subgraph, specifically:
  • a meta-path includes node types and edge types alternately arranged in order. Among them, the node types are ranked first and last. The order of the node types and edge types expresses the structure of heterogeneous subgraphs;
  • the splitting a heterogeneous graph into at least two heterogeneous subgraphs according to at least two preset meta-paths specifically includes:
  • the preset graph convolution model learns the sample data according to the heterogeneous subgraph to obtain the vector expression of the nodes in the heterogeneous subgraph, which specifically includes:
  • the preset graph convolution model learns the sample data according to the attribute information of each node in the heterogeneous subgraph and the structure information and attribute information of the at least first-order neighbor nodes of each node in the heterogeneous subgraph, to obtain Vector expression of nodes in the heterogeneous subgraph.
  • the preset graph convolution model is based on the attribute information of each node in the heterogeneous subgraph and the structure information and attributes of at least first-order neighbor nodes of each node in the heterogeneous subgraph.
  • Information, learning the sample data to obtain the vector expression of each node in the heterogeneous subgraph specifically including:
  • the preset graph convolution model performs an N-layer convolution operation according to the attribute information of the node and the attribute information and structure information of the first to Nth-order neighbor nodes to obtain the vector expression of the node.
  • the preset graph convolution model is based on the attribute information of each node in the heterogeneous subgraph and the structure information and attributes of at least first-order neighbor nodes of each node in the heterogeneous subgraph.
  • Information, learning the sample data to obtain the vector expression of each node in the heterogeneous graph specifically including:
  • the preset graph convolution model performs an N-layer convolution operation according to the attribute information of the node and the attribute information and structure information of the first to Nth-order neighbor nodes after sampling to obtain the vector expression of the node.
  • the preset aggregation model aggregates vector expressions of the same node in different heterogeneous subgraphs based on sample data to obtain the same vector expression of the same node in different heterogeneous subgraphs, Specifically:
  • the preset aggregation model is based on the sample data, and uses an attention mechanism or a fully connected aggregation mechanism or a weighted average aggregation mechanism to aggregate the vector expressions of the same node in different heterogeneous subgraphs to obtain the same The same vector representation of the node.
  • the embodiment of the present invention also provides a system for obtaining expressions of relationships between entities, including: a registration device, a storage device, a calculation device, and a parameter exchange device;
  • Storage device for storing data of heterogeneous sub-graphs
  • the computing device is used to obtain the data of the heterogeneous subgraph from the storage device through the registration device, and learn the sample data based on the heterogeneous graph by using the above-mentioned method of obtaining the relationship expression between entities to obtain the low-dimensionality of each node in the heterogeneous graph Vector expression
  • the parameter exchange device is used for parameter interaction with the computing device.
  • the graph convolution model is used to learn the sample data, and the vector expressions of the same nodes obtained by the learning of each heterogeneous subgraph are merged, and the vector expressions of the same nodes are
  • the fusion result optimizes the parameters of the machine learning model, which is used to learn the next batch of samples, realize the iterative learning of the samples, and finally obtain the low-dimensional vector expression for the nodes in the heterogeneous graph, thereby reducing the heterogeneous graph learning process
  • the speed and efficiency of heterogeneous graph learning are improved.
  • the heterogeneous graph learning method is used in the advertisement search scene, mining the entity relationship in the advertisement search scene to realize the use of a large amount of information to accurately realize the advertisement recall, improve the quality of the advertisement recall, and use all advertisements as candidates to ensure that it can be recalled under any traffic Enough advertisements can be achieved in one step through the vector method.
  • FIG. 1 is a flowchart of a method for obtaining expressions of relationships between entities in Embodiment 1 of the present invention
  • FIG. 2 is a schematic diagram of the principle of a method for obtaining expressions of relationships between entities in Embodiment 2 of the present invention
  • Embodiment 3 is a flowchart of a method for obtaining expressions of relationships between entities in Embodiment 2 of the present invention
  • Figure 4a is an exemplary diagram of a heterogeneous graph constructed in Embodiment 2 of the present invention.
  • Figure 4b is another example diagram of a heterogeneous graph constructed in Embodiment 2 of the present invention.
  • FIG. 5 is an exemplary diagram of splitting a heterogeneous graph into heterogeneous subgraphs in Embodiment 2 of the present invention.
  • FIG. 6 is an example diagram of a convolutional network model of heterogeneous subgraphs in the second embodiment of the present invention.
  • Fig. 7 is an example diagram of neighbor node sampling in the second embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a system for obtaining an expression of a relationship between entities in an embodiment of the present invention.
  • Fig. 9 is a schematic structural diagram of an advertisement recall system in an embodiment of the present invention.
  • Graph learning has a wide range of applications in mining various data relationships in the real world. For example, it is used in search advertising platforms to mine the correlation between search requests and advertisements and click-through-rate (CTR). That is to say, the method of the present invention can be used in the field of advertisement search for the recall of search advertisements.
  • Search advertising refers to advertisements that advertisers determine relevant keywords based on the content and characteristics of their products or services, write advertising content, and independently place prices in the search results corresponding to the keywords.
  • Search ads recall refers to the selection of the most relevant ads from a large collection of ads through a certain algorithm or model.
  • Existing search ad recall technologies may screen "high-quality" advertisements based on the degree of matching between query terms and advertiser bid words, the advertiser's purchase price, and users' statistical preferences for advertisements; or add each user's Historical behavior data, personalized matching recall of ads.
  • the inventor found in the research of the prior art that the existing recall technology either only emphasizes the matching degree between the advertisement and the query word, or only emphasizes the improvement of the recall advertisement revenue, and lacks an integrated model to take both of the two. Since the quality of advertisement recall is very important to search advertisement revenue and user experience, the inventor provided a graph learning technology that can be used to obtain expressions of relationships between entities in the advertisement recall process, which can obtain more high-quality, Users are more concerned about the ad recall collection.
  • the first embodiment of the present invention provides a method for obtaining expressions of relationships between entities.
  • the process is shown in FIG. 1, and includes the following steps:
  • Step S101 Divide the pre-built heterogeneous graph into at least two heterogeneous subgraphs according to the pre-defined meta-path.
  • the meta-path is used to express the structure of the heterogeneous subgraph and the types of nodes and edges included in the heterogeneous subgraph. .
  • a meta path corresponds to a heterogeneous subgraph.
  • the meta path is used to express the structure of the heterogeneous subgraph and the node types and edge types included in the heterogeneous subgraph are specifically: a meta path is used to express the structure of a heterogeneous subgraph and The node type and edge type included in the heterogeneous subgraph.
  • a meta-path includes node types and edge types alternately arranged in order. Among them, the node type is ranked first and last. The order of the node type and edge type expresses the heterogeneous subgraph structure.
  • Splitting the heterogeneous graph into at least two heterogeneous subgraphs according to a preset meta-path specifically includes splitting the heterogeneous graph into at least two heterogeneous subgraphs according to at least two preset meta-paths.
  • the corresponding type of node in the heterogeneous graph is obtained according to the node type included in the meta-path; according to the type of the edge connecting each adjacent node, from Obtain the edges that meet the requirements in the heterogeneous graph; the obtained nodes of the corresponding type and the edges that meet the requirements form the heterogeneous subgraph corresponding to the meta-path.
  • Step S102 Acquire sample data of a batch.
  • the sample data can be divided into multiple batches and learn in batches based on heterogeneous subgraphs.
  • Step S103 The preset graph convolution model learns a batch of sample data (or sample data set) according to the heterogeneous subgraph to obtain the vector expression of the nodes in the heterogeneous subgraph, a graph convolution model Corresponds to a heterogeneous subgraph.
  • the preset graph convolution model learns the sample data according to the attribute information of each node in the heterogeneous subgraph and the structure information and attribute information of the at least first-order neighbor nodes of each node in the heterogeneous subgraph , Get the vector expression of the nodes in the heterogeneous graph.
  • One is to learn and process the sample data based on all nodes in the heterogeneous subgraph, including:
  • the preset graph convolution model performs an N-layer convolution operation according to the attribute information of the node and the attribute information and structure information of the first to Nth-order neighbor nodes to obtain the vector expression of the node.
  • the first to Nth order neighbor nodes of the node sample the neighbor nodes of the same order according to the preset number according to the weight of the edges between the nodes, to obtain the first to Nth order neighbor nodes after sampling;
  • the preset graph convolution model performs an N-layer convolution operation according to the attribute information of the node and the attribute information and structure information of the first to Nth-order neighbor nodes after sampling to obtain the vector expression of the node.
  • Step S104 The preset aggregation model aggregates the vector expressions of the same node in different heterogeneous subgraphs based on the sample data to obtain the same vector expression of the same node in different heterogeneous subgraphs.
  • the preset aggregation model is based on sample data, using attention mechanism aggregation learning, fully connected aggregation learning, or weighted average aggregation learning to aggregate the vector expressions of the same node in different heterogeneous subgraphs to obtain the same node in different heterogeneous subgraphs The same vector expression of.
  • Step S105 The preset loss function optimizes the parameters of the model based on the sample data and the same vector expression of the same node.
  • the vector expressions of at least two types of the same node are used to converge to obtain the low-dimensional vector expression of the virtual request node; the virtual request node is through a certain association relationship A virtual node constructed by at least two types of nodes; according to the low-dimensional vector expression of the virtual request node and the low-dimensional vector expression of another type of node, determine the associated parameters between at least three types of nodes, and according to the associated parameters Optimize model parameters.
  • Step S106 Whether the sample data of all batches have been acquired, if not, go to step 107; if so, go to step S108.
  • Step 107 Obtain sample data of the next batch, and return to step S103.
  • Step S108 Obtain a low-dimensional vector expression of each node in the heterogeneous graph.
  • a node in the heterogeneous graph corresponds to an entity in the sample data.
  • a low-dimensional vector expression of each node in the composition can be obtained.
  • a low-dimensional vector expression of each node in the composition is the last batch of samples learned, and the aggregation model is The same vector representation of the same node in different heterogeneous subgraphs.
  • the matching degree is the correlation parameter between the nodes obtained last time by the loss function.
  • the machine learning model is used to learn the sample data, and the vector expressions of the same nodes obtained by the learning of each heterogeneous subgraph are merged, and According to the fusion result of the vector expression of the same node, the parameters of the machine learning model are optimized, which is used to learn the next batch of samples, realize the iterative learning of the samples, and finally obtain the low-dimensional vector expression for the nodes in the heterogeneous graph.
  • the second embodiment of the present invention provides a specific implementation process of a method for obtaining expressions of relationships between entities.
  • the process of implementing advertisement recall in a search advertisement scenario is taken as an example for description.
  • the implementation principle of the method is shown in FIG. 2 and the flow is shown in FIG. As shown in 3, including the following steps:
  • Step S301 Construct a heterogeneous graph.
  • a large-scale heterogeneous graph is constructed for the search recall scene based on user logs and related products and advertisement data, which serves as a rich search interaction graph for the advertisement search scene, and the constructed heterogeneous graph is used as the follow-up Graph data input, such as the graph data of the heterogeneous graph at the bottom in Figure 2.
  • the heterogeneous graph includes multiple types of nodes such as Query, Item, and Ad to represent different entities in the search scenario.
  • the heterogeneous graph includes multiple types of Edges to represent multiple relationships between entities. Among them, the node type and its specific meaning can be shown in Table 1 below, and the edge type and its meaning can be shown in Table 2 below.
  • the Query node and the Item node are used as user intention nodes to describe the user's personalized search intention
  • the Ad node is the advertisement placed by the advertiser.
  • the user behavior edge represents the user's historical behavior preference. For example, you can create a "click edge" between the Query node and the Item node or between the Query node and the Ad node and use the number of clicks as the edge weight to indicate Query and Item/ Clicks between Ad; For example, you can create a common click edge (session edge), which means the item or Ad that is clicked in the same session (time period) and Query; For example, you can also create a collaborative filtering edge (cf edge) to represent different nodes Collaborative filtering relationship.
  • user behavior describes a dynamic relationship. Popular nodes (such as high-frequency Query nodes) will have more displays and clicks, and then have more dense The unpopular nodes and new nodes will have relatively sparse variable relationships and smaller edge weights, so user behavior edges can better describe popular nodes.
  • the content similarity edge (semantic edge) is used for the similarity between customer nodes. For example, an edge is established between Item nodes and the text similarity of its title is used as the weight.
  • the content-similar edges reflect a static relationship between nodes, which is more stable, and can also well describe the relationship between unpopular nodes and new nodes.
  • the attribute similarity edge represents the overlap of domains between nodes, such as brand, category and other domains.
  • Figure 4b is a representation of the constructed heterogeneous graph, where nodes with the same shape represent nodes of the same type, and edges with the same linear shape represent edges of the same type.
  • Step S302 Divide the constructed heterogeneous graph into at least two heterogeneous subgraphs according to the preset meta-path.
  • the meta-path is used to express the structure of the heterogeneous subgraph and the node types and edge types included in the heterogeneous subgraph.
  • the graph data to be learned in this application is essentially a heterogeneous graph, and there may be multiple types of points and multiple types of edges.
  • the current graph convolutional neural network (GCN) is only suitable for isomorphic graphs.
  • the use of graph convolutional neural networks for learning as isomorphic graphs cannot obtain effective low-dimensional vector expression. Therefore, in order to realize the learning of heterogeneous graphs, some meaningful meta-paths are defined to divide the original large heterogeneous graph into multiple meaningful heterogeneous subgraphs for learning.
  • the defined meta path can be shown in Table 3 below.
  • the constructed heterogeneous graph is split.
  • the heterogeneous graph shown in Figure 4b is split.
  • meta-path a Meta-path b
  • Meta-path c Meta-path d
  • Meta-path e Meta-path f
  • a sub-graph b
  • sub-graph c sub-graph d
  • sub-graph e sub-graph f
  • sub-graph f Heterogeneous subgraphs.
  • meta-path a includes node Item/Ad-joint click edge-node Item/Ad-attribute similar edge-node Item/Ad.
  • subgraph a is constructed according to meta-path a, from the heterogeneous graph constructed Obtain the nodes (Item and Ad) of the corresponding node type in the meta path a, and keep the edges that meet the requirements to obtain the subgraph a.
  • the construction of heterogeneous subgraphs corresponding to other meta-paths is similar to meta-path a, and will not be repeated here.
  • the bottom is a heterogeneous graph constructed, based on the heterogeneous graph, the initial vector expression of each node is formed according to the characteristics of each node.
  • For each specified node define a meta-path containing the specified node, and construct a heterogeneous subgraph based on the defined meta-path.
  • two meta-paths are defined for the search advertisement node (Ad), corresponding Split into two heterogeneous subgraphs; define four meta-paths for the query node (Query), and split four heterogeneous subgraphs accordingly; for k commodities such as 1, 2, ..., k (Item) node, each commodity node defines two meta-paths, and splits two heterogeneous subgraphs accordingly.
  • Step S303 Obtain sample data of a batch.
  • Extract sample data related to advertisement search from user log data can come from user historical behavior logs, commodity basic attribute information table, advertisement basic attribute information table, query word basic attribute information table, etc.
  • the sample data of each batch is sequentially input into the machine learning model for training and learning.
  • the learning results of the previous batch can optimize the parameters of the model, and use the optimized parameters for the learning of the sample data of the next batch to achieve The effect of iterative learning to obtain the final learning result.
  • Step S304 The preset graph convolution model learns a batch of sample data according to heterogeneous subgraphs to obtain vector expressions of nodes in heterogeneous subgraphs, and one graph convolution model corresponds to one heterogeneous subgraph.
  • each heterogeneous sub-graph corresponds to a graph convolutional network model.
  • the two graph convolutional network models in the leftmost group in Figure 2 correspond to the two defined search advertisement nodes (Ad) respectively.
  • the two heterogeneous sub-graphs split by a meta-path, the four graph convolutional network models in the second group from the left correspond to the four heterogeneous sub-graphs split from the four meta-paths defined by the query word node (Query).
  • Composition graph; 1,..., k groups of graph convolutional network models on the right the two graph convolutional network models in each group correspond to two meta-paths defined by an item (Item) node Heterogeneous subgraphs.
  • the sample data is used as input to correspond to the corresponding node in the heterogeneous subgraph for learning.
  • Each convolutional network model shown in Figure 2 can share ownership.
  • a heterogeneous subgraph traverse the sample data, read the recorded entity for a piece of sample data currently traversed, and find the corresponding node of the entity in the heterogeneous graph; from the heterogeneous subgraph that includes the node In, read the first to Nth order neighbor nodes of the node, N is a preset positive integer; the preset graph convolution model is based on the attribute information of the node and the first to Nth order neighbor nodes after sampling The attribute information and structure information are subjected to N-layer convolution operation to obtain the vector expression of the node.
  • the N-layer convolution operation is specifically: for a node in the heterogeneous subgraph, obtain its N-order neighbor node, and then perform the convolution operation hierarchically, for the N-1 order neighbor node, pair with the N-1 order neighbor node
  • the vector expression of the N-order neighbor nodes connected by the node is convolved to obtain the neighbor low-dimensional vector expression of the N-1 order neighbor node.
  • the neighbor low-dimensional vector expression of the N-1 order neighbor node and the N-1 order neighbor node The original low-dimensional vector expression is combined to obtain the new low-dimensional vector expression of the N-1 order neighbor node; and so on, ..., convolution operation is performed on the vector expression of the second order neighbor node connected to the first order neighbor node , Get the neighbor low-dimensional vector expression of the first-order neighbor node, combine the neighbor low-dimensional vector expression of the first-order neighbor node and the original low-dimensional vector expression of the first-order neighbor node to obtain the new low-dimensional vector of the first-order neighbor node Expression; perform convolution operation on the low-dimensional vector expression of each first-order neighbor node of the node to obtain the low-dimensional vector expression of the node's neighbors, and perform the low-dimensional vector expression of the node's neighbors and the original low-dimensional vector expression of the node Combining operations, the new neighbor low-dimensional vector expression of the node is obtained.
  • Fig. 6 The principle of learning sample data based on a heterogeneous subgraph is shown in Fig. 6.
  • a graph convolutional network can be constructed as shown in Fig. 6.
  • the first-order neighbor nodes of node 1 in subgraph a have 2, 3, 4, and 6, and the second-order neighbor nodes have 1, 2, 3, 4, and 10.
  • the second-order neighbor nodes 1, 2, 3, 4, and 10 of node 1 in the subgraph pass through the graph convolution layer to obtain the low-dimensional vector representations of the neighbor nodes 2, 3, 4, and 6 of the first-order neighbor nodes.
  • the low-dimensional vector expressions of 4 and 6 are spliced and non-linearly transformed to obtain the final low-dimensional vector expressions of nodes 2, 3, 4, and 6, which are used as input through the graph convolution layer, and the original low-dimensional vector expression of node 1 is spliced ,
  • the final low-dimensional vector expression of the second-order graph convolutional network of node 1 is obtained by conversion.
  • the final low-dimensional vector expression of other nodes is obtained in a manner similar to that of node 1, and will not be repeated here.
  • the isolated node 8 without neighbor nodes retains the original vector expression. Based on a similar approach, the final low-dimensional vector expression of each node in each heterogeneous subgraph can be obtained.
  • the meta-path-based graph convolution advertisement recall scheme can effectively solve the advertisement recall scenario by using the graph convolution method, there is still the problem of calculation amount.
  • the number of neighbor nodes of a node increases exponentially with the increase of the number of graph convolutional layers.
  • Node 1 has 3 first-order neighbors and 9 second-order neighbors.
  • the hierarchical neighbors can be sampled based on beam-search, reducing the neighbor space complexity from O(n k ) to O(kn).
  • the neighbor nodes when learning the sample data based on the heterogeneous subgraph, when there are many nodes in the heterogeneous subgraph, the neighbor nodes can be sampled, and the convolution calculation is performed based on the neighbor nodes in the sample.
  • a heterogeneous subgraph as an example, traverse the sample data, read the recorded entity for a piece of sample data currently traversed, and find the corresponding node of the entity in the heterogeneous graph; from the heterogeneous subgraph that includes the node
  • N is a preset positive integer
  • the first to Nth order neighbor nodes of the node are compared to neighbors of the same order according to the weight of the edges between nodes
  • the nodes sample according to the preset number to obtain the first to Nth order neighbor nodes after sampling; the preset graph convolution model is based on the attribute information of the node and the attribute information of the first to Nth order neighbor nodes after sampling Perform N-layer convolution with the structure information
  • the sum of the edge weights of neighbor nodes is used as the weight, and neighbor weighted sampling is performed on the nodes.
  • the principle of sampling based on edge weights is shown in FIG. 7.
  • the original convolution structure of node 1 is shown in the left figure in FIG. 7, and the weight of each edge is shown in the label number of each edge in the figure.
  • weighted sampling can be performed based on the node weight w to obtain k sampling nodes, and the weight w can be Expressed as:
  • L represents the current weight of the heavy layer node v
  • J I v represents the node number of the nodes have an upper edge
  • l l represents layer
  • i and j is a sequence number for the specified node.
  • Layer node sampling can reduce the growth trend of neighbor nodes from exponential level to linear level on the basis of taking into account all the connection relationships of upper neighbor nodes.
  • Step S305 The preset aggregation model is based on the vector expression of the nodes in the heterogeneous subgraph, and the sample data is aggregated and learned to obtain the same vector expression of the same node in different heterogeneous subgraphs.
  • the same node may exist in different heterogeneous subgraphs.
  • node 1 exists in subgraphs a, b, c, e, and f, and different heterogeneous subgraph convolutional neural networks will get different node vector expressions.
  • the attention mechanism or the fully connected aggregation mechanism or the weighted average aggregation mechanism is used to aggregate the vector expressions of the same node in different heterogeneous subgraphs, and the same vector expression of the same node in different heterogeneous subgraphs is obtained, which is to aggregate the weighted result As the final node low-dimensional vector expression (embedding) result.
  • the process of converging vector expressions of the same node in different heterogeneous graphs includes:
  • the adjusted convolution model is as follows:
  • WEIGHTEDMEAN represents the weighted average
  • N represents the neighbors of the node v that meets metapath s k
  • w represents the weight in the weighted average
  • CONCAT represents the direct concatenation of the two vectors.
  • W represents the weight to be learned
  • ⁇ represents the nonlinear transformation.
  • Step S306 The preset loss function optimizes the parameters of the model based on the sample data and the same vector expression of the same node.
  • low-dimensional vector expressions of advertisements, products, and query words can be obtained.
  • the user’s current query term and the user’s previously clicked advertisement or product are used as the user’s current search request.
  • the attention mechanism is used to express the low-dimensional vector of the query term (H Q )
  • multiple low-dimensional vector expressions of pre-clicks H 1k , ..., H Ik ) are aggregated into the final user search request vector.
  • the ads that are clicked under the current request are regarded as positive examples, and the ads that are not clicked are regarded as negative examples.
  • the sample structure is as follows: (request, ad, click-label), including Requests, search ads, and click labels.
  • the request request (query, ⁇ realtimeclicked items ⁇ ads ⁇ k ), including search advertisements and multiple real-time clicked products.
  • y i represents the label data
  • p i represents the prior probability
  • v request , v ad represent the vector expression of the virtual request node and the advertising node
  • R(v request , v ad ) represents the vector expression of the virtual request node and the advertising node
  • Step S307 Whether the sample data of all batches have been acquired, if not, go to step 308; if yes, go to step S309.
  • Step S308 Obtain the sample data of the next batch, and return to step S304.
  • Step S309 Obtain a low-dimensional vector expression of each node in the heterogeneous graph.
  • a node in the heterogeneous graph corresponds to an entity in the sample data.
  • a node in the heterogeneous graph corresponds to an entity in the sample data.
  • the smallest edge is a schematic representation of a heterogeneous graph.
  • the four rows of small white squares in the upper layer are the node vectors in the heterogeneous graph.
  • the initial vector expression of each node is obtained, and then input into the learning model corresponding to each heterogeneous subgraph.
  • a batch of sample data After learning, update the vector expression of each node in the heterogeneous subgraph according to the learning result, converge the vector expression of the same node in each heterogeneous subgraph, and obtain a converged vector expression of the same node, for example, in Figure 2 Is the converged vector expression of search advertising nodes, Is the convergent vector expression of query term nodes, It is the vector expression after the convergence of each commodity node.
  • the embodiments of the present invention also provide a system for obtaining expressions of relationships between entities.
  • the system can be set up in network equipment, cloud equipment in the cloud, or architecture server equipment, client equipment and other equipment.
  • the structure of the system is shown in FIG. 8 and includes: a registration device 803, a storage device 801, a calculation device 802, and a parameter exchange device 804.
  • the storage device 801 is used to store data of heterogeneous subgraphs
  • the computing device 802 is configured to obtain the data of the heterogeneous subgraph from the storage device 801 through the registration device 803, and learn the sample data based on the heterogeneous graph by using the above-mentioned method of obtaining the relationship expression between entities to obtain each node in the heterogeneous graph The low-dimensional vector expression of.
  • the parameter exchange device 804 is used for parameter interaction with the computing device.
  • the computing device 802 obtains the data of each node and edge from the storage device through the registration device 803, including:
  • the computing device 802 sends a data query request to the registration device 803, the data query request includes the information of the heterogeneous subgraph to be queried; receives the query result returned by the registration device 803, and the query result includes the storage device information storing the heterogeneous subgraph data ; Obtain heterogeneous subgraph data from the corresponding storage device 801 according to the storage device information.
  • the storage device 801 may also store data and sample data of each node and edge in the heterogeneous graph.
  • the computing device 802 sends a data query request to the registration device 803, the data query request includes the information of the node and edge to be queried; receiving the query result returned by the registration device 803, the query result includes the storage device information storing the data of the node and edge; Obtain the data of each node and edge from the corresponding storage device 801 according to the storage device information.
  • an embodiment of the present invention also provides an advertisement recall system. As shown in FIG. 9, it includes a system 901 for obtaining relationship expressions between entities and an advertisement recall matching system 902;
  • the system 901 for obtaining expressions of relationships between entities is used to construct a heterogeneous graph for advertisement search scenarios.
  • the node types in the heterogeneous graph include: at least one of advertisements, commodities, and query terms, and the type of edges Including at least one of a click side, a common click side, a collaborative filtering side, a content semantically similar side, and an attribute similar side;
  • the preset graph convolution model learns a batch of sample data according to heterogeneous subgraphs to obtain the vector expression of nodes in heterogeneous subgraphs.
  • a graph convolution model corresponds to a heterogeneous subgraph;
  • the preset aggregation model is based on sample data and aggregates the vector expressions of the same node in different heterogeneous subgraphs to obtain the same vector expression of the same node in different heterogeneous subgraphs;
  • the preset loss function optimizes the parameters of the model based on the same vector expression of the sample data and the same node;
  • a node of corresponds to an entity in the sample data
  • the advertisement recall matching system 902 is used to use the low-dimensional vector expressions of query term nodes, product nodes and search advertisement nodes obtained by the system for obtaining relationship expressions between entities to determine the degree of matching between query term nodes, commodity nodes and search advertisement nodes , According to the matching degree, select the search advertisement that matches the product and the query term with the set requirement.
  • the meta-path defined by the system for obtaining the expression of the relationship between entities one meta-path corresponds to a heterogeneous subgraph, and the meta-path is used to express the structure of the heterogeneous subgraph and the node types and edges included in the heterogeneous subgraph
  • the specific type is: a meta-path is used to express the structure of a heterogeneous subgraph and the node types and edge types included in the heterogeneous subgraph; specifically: a meta-path includes node types and edge types alternately arranged in order, Among them, the node type is ranked first and last, and the arrangement order of node type and edge type expresses the structure of heterogeneous subgraph;
  • the system for obtaining the expression of the relationship between entities divides the heterogeneous graph into at least two heterogeneous subgraphs according to a preset meta-path specifically including: dividing the heterogeneous graph into at least two preset meta-paths The graph is split into at least two heterogeneous subgraphs, specifically for each of the at least two preset meta-paths, the corresponding type in the heterogeneous graph is obtained according to the node type included in the meta-path Nodes; According to the types of edges connecting adjacent nodes, obtain the required edges from the heterogeneous graph; the obtained corresponding types of nodes and the required edges constitute the heterogeneous sub-path corresponding to the meta path Figure.
  • the system for obtaining the expression of the relationship between entities learns the sample data according to the heterogeneous subgraph through a preset graph convolution model to obtain the vector expression of the nodes in the heterogeneous subgraph, which specifically includes: a preset
  • the graph convolution model obtains the vector expression of the nodes in the heterogeneous graph according to the attribute information of each node in the heterogeneous subgraph and the structure information and attribute information of the at least first-order neighbor nodes of each node in the heterogeneous subgraph.
  • the system for obtaining the expression of the relationship between entities uses a preset graph convolution model according to the attribute information of each node in the heterogeneous subgraph and the structural information of at least first-order neighbor nodes of each node in the heterogeneous subgraph and Attribute information to obtain the vector expression of each node in the heterogeneous graph, which specifically includes:
  • the preset graph convolution model performs an N-layer convolution operation according to the attribute information of the node and the attribute information and structure information of the first to Nth order neighbor nodes to obtain the vector expression of the node.
  • the system for obtaining the expression of the relationship between entities uses a preset graph convolution model according to the attribute information of each node in the heterogeneous subgraph and the structural information of at least first-order neighbor nodes of each node in the heterogeneous subgraph and Attribute information to obtain the vector expression of each node in the heterogeneous graph, which specifically includes:
  • the preset graph convolution model performs an N-layer convolution operation according to the attribute information of the node and the attribute information and structure information of the first to Nth-order neighbor nodes after sampling to obtain the vector expression of the node.
  • the system for obtaining expressions of relationships between entities aggregates vector expressions of the same node in different heterogeneous subgraphs based on sample data through a preset aggregation model to obtain the same vector expression of the same node in different heterogeneous subgraphs , Specifically including:
  • the preset aggregation model is based on the sample data, using attention mechanism aggregation learning, fully connected aggregation learning, or weighted average aggregation learning to aggregate the vector expressions of the same node in different heterogeneous subgraphs to obtain different heterogeneous subgraphs.
  • attention mechanism aggregation learning fully connected aggregation learning
  • weighted average aggregation learning to aggregate the vector expressions of the same node in different heterogeneous subgraphs to obtain different heterogeneous subgraphs.
  • the same vector representation of the same node in the graph is based on the sample data, using attention mechanism aggregation learning, fully connected aggregation learning, or weighted average aggregation learning to aggregate the vector expressions of the same node in different heterogeneous subgraphs to obtain different heterogeneous subgraphs.
  • the same vector representation of the same node in the graph is based on the sample data, using attention mechanism aggregation learning, fully connected aggregation learning, or weighte
  • the advertisement recall matching system determines the degree of matching between query term nodes, product nodes and search advertisement nodes, including:
  • the virtual request node is a virtual node constructed by the query term node and the commodity node pre-clicked by the user under the common query term;
  • the matching degree between the query term node, the product node and the search advertisement node is determined.
  • the advertisement recall matching system selects search advertisements that match the product and query terms according to the matching degree, including:
  • a search advertisement whose distance meets the set requirement is selected.
  • An embodiment of the present invention also provides a computer-readable storage medium on which computer instructions are stored, and when the instructions are executed by a processor, the foregoing method for obtaining expressions of relationships between entities is implemented.
  • An embodiment of the present invention also provides a heterogeneous graph learning device, including: a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the above-mentioned acquisition entity when the program is executed.
  • the method of expressing the relationship includes: a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the above-mentioned acquisition entity when the program is executed. The method of expressing the relationship.
  • terms such as processing, calculation, operation, determination, display, etc. may refer to one or more actions and/or processes of processing or computing systems, or similar devices, and the actions and/or processes will be expressed as The data manipulation and conversion of physical (such as electronic) quantities in the registers or memory of the processing system becomes other data similarly represented as physical quantities in the memory, registers or other such information storage, transmission or display devices of the processing system.
  • Information and signals can be represented using any of a variety of different technologies and methods.
  • the data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.
  • the steps of the method or algorithm described in combination with the embodiments of this document can be directly embodied as hardware, a software module executed by a processor, or a combination thereof.
  • the software module can be located in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM or any other form of storage medium known in the art.
  • An exemplary storage medium is connected to the processor, so that the processor can read information from the storage medium and can write information to the storage medium.
  • the storage medium may also be a component of the processor.
  • the processor and the storage medium may be located in the ASIC.
  • the ASIC can be located in the user terminal.
  • the processor and the storage medium may also exist as discrete components in the user terminal.
  • the technology described in this application can be implemented with modules (for example, procedures, functions, etc.) that perform the functions described in this application.
  • These software codes can be stored in a memory unit and executed by a processor.
  • the memory unit may be implemented in the processor or outside the processor. In the latter case, it is communicatively coupled to the processor through various means, which are well known in the art.

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Abstract

L'invention concerne un procédé, un système et un dispositif permettant d'obtenir une expression d'une relation entre des entités, et un système de récupération de publicité. Le procédé consiste : à diviser, selon un méta-trajet, un graphe hétérogène en au moins deux sous-graphes hétérogènes, et à obtenir un lot de données d'échantillon ; à apprendre les données d'échantillon selon les sous-graphes hétérogènes de façon à obtenir des expressions vectorielles de nœuds dans les sous-graphes hétérogènes ; à agréger, sur la base des données d'échantillon, des expressions vectorielles de nœuds identiques dans différents sous-graphes hétérogènes de façon à obtenir une même expression vectorielle pour les nœuds identiques dans les différents sous-graphes hétérogènes ; à réaliser une optimisation sur un paramètre d'un modèle sur la base des données d'échantillon et de la même expression vectorielle pour les nœuds identiques ; et à obtenir le lot suivant de données d'échantillon et l'apprendre jusqu'à ce que tous les lots de données d'échantillon aient été appris, de façon à obtenir une expression vectorielle à faible dimension de chaque nœud dans le graphe hétérogène. Le procédé permet l'apprentissage de graphes hétérogènes complexes, assure une vitesse de traitement élevée et une grande efficacité, et peut être utilisé dans une publicité de recherche pour améliorer le degré de correspondance pour des publicités récupérées.
PCT/CN2020/070249 2019-01-16 2020-01-03 Procédé, système et dispositif pour obtenir une expression de relation entre des entités, et système de récupération de publicité Ceased WO2020147594A1 (fr)

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