WO2013075745A1 - Procédé et système d'élaboration de modèles d'utilisateurs - Google Patents

Procédé et système d'élaboration de modèles d'utilisateurs Download PDF

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WO2013075745A1
WO2013075745A1 PCT/EP2011/070873 EP2011070873W WO2013075745A1 WO 2013075745 A1 WO2013075745 A1 WO 2013075745A1 EP 2011070873 W EP2011070873 W EP 2011070873W WO 2013075745 A1 WO2013075745 A1 WO 2013075745A1
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tree
user
node
shaped data
data structure
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English (en)
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Jöran BEEL
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GENZMEHR Marcel
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GENZMEHR Marcel
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    • 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0282—Rating or review of business operators or products

Definitions

  • the invention relates to a method and a system for creating user models and recommendations based thereon, preferably by analyzing tree-shaped data structures.
  • referral services Often, such user models are used by referral services. Depending on the interests of a user, these referral services will display individual recommendations for, for example, movies, books, music, or advertising tailored to the user.
  • a recommendation service is always a so-called "User-Item Matching Problem": the question with this The problem is, which small selection of relevant items (eg music, websites, books, etc.) from a large amount of available items should be recommended to a user.
  • This known from the prior art approach is shown in Fig. 1. Shown in Figure 1 is a set of users (User 1 to User 3) and a set of items (Item 1 to Item 3). With appropriate procedures, the relevance of users and items to each other is calculated.
  • CBF Content Based Filtering
  • An item can be any object.
  • An item may be a document (books, web pages, emails, etc.), a multimedia object (movies, music, photos), a person or a place.
  • items can also be menu entries of a computer application or components of graphical user interfaces.
  • a user is associated with an item if any reference exists between them. This means that if the user has for example read, bought or briefly reviewed a book, knows a person or has watched, downloaded or rated a film on a film portal, the user stands with the book, the person or the person Movie in connection.
  • the connection can weighted differently, depending on the type of connection. For example, buying a book could be more weighted than just looking at the book cover. Or a connection to an item can be weighted more, the more often the item was used.
  • Content based filtering uses the content of connected objects to create a user model.
  • this method is applied to textual items, ie documents, since the content of documents (ie the text) can be processed well by computers in contrast, eg. B. to pictures.
  • a model is created for each item.
  • the so-called "Vector Space Model” is often used, a model that displays documents as vectors of their terms. Each vector expresses by its length, how well the corresponding term describes the actual document. This weighting can be calculated using various methods.
  • One common method is the so-called TF-IDF method.
  • the weighting of a term for a document is the greater the more frequently the term occurs in the document and the fewer documents in the entire collection there are with this term.
  • the user model is then generated from the models of the various connected items. This means that if a user has many books that contain the term "recommender" with a high weight, then the user model also gets assigned this term with a high weight.
  • the different item models can be incorporated into the user model with different weightings.
  • the user model is stored in the same format as the item models - for example, as a Vector Space Model which will later be recommended to the user, a model is also created, for example, again with TF-IDF method and the Vector Space Model.
  • These items may not necessarily be the same items that are associated with users.
  • the matching of the user models with the items to be recommended is preferably based on similarity comparisons between the user and the item models. For text-based items, if the user model contains the same high-weight terms as the item models of the items to be recommended, then the similarity is large and the item is recommended to the user.
  • a common similarity measure in the Vector Space Model is about the Cosine Similarity.
  • CBF Content Based Filtering
  • the object of the invention is therefore to provide a method and a system which allow in a simple manner item models or user models also for hierarchical, i. Create tree-shaped structures to build user models and recommendations based on them.
  • the elements associated with the nodes are determined, the elements representing a content of the respective node,
  • a user model is generated, wherein the generated user model comprises the determined elements and the element weight assigned to the respective element.
  • the nodes of the tree-shaped data structure can be weighted and each node can be assigned a node weighting.
  • each element can be assigned a predetermined element weighting or the node weighting of the associated node.
  • the method may further include a preprocessing step in which
  • - nodes are deleted, which have or do not have predetermined attributes, and / or
  • the weighting of the nodes may include static node weighting and / or dynamic node weighting, where
  • the number of child nodes assigned to the respective node in the case of static node management, the number of child nodes assigned to the respective node, the number of the respective sibling nodes, the depth of the respective node in the tree-shaped data structure, the visibility of the node, or a combination thereof are taken into account, and
  • the dynamic node weighting for each node the age, the time of the last change, the number of changes, the number of shifts within the tree-shaped data structure, the number of markers, the visibility of the node, an attenuation factor, or a combination thereof be taken into account.
  • Determining the elements associated with the nodes may include preprocessing the determined elements, wherein the pre-processing of the elements decomposes text into tokens and / or terms, provided that the element is a text element and / or references are processed, if the element is a reference element ,
  • the element weights assigned to the elements in the initialization step may be adjusted, where, when adjusting the respective element weights, the element type, attribute values of the attributes associated with the element, a frequency of the element within the tree-shaped data structure, the number of tree-shaped data structures in a collection of tree-shaped ones Data structures in which the element occurs, a frequency of the element within a collection of tree-shaped data structures, the size of the tree-shaped data structure relative to other tree-shaped data structures in a collection of tree-shaped data structures, the position of the element within the node, the language of the element, the Number of elements within the node, the distance of the element to similar elements of other nodes, frequency of the element in the path between the node and the root node
  • the generated user model may be stored in a storage device to be provided to the recommendation service. All elements can be stored together with the respective element weightings as a user model, or for each element type a separate user model can be stored, whereby the user models of the different element types form an overall user model. In the case of a plurality of tree-shaped data structures that can be assigned to the user, a number of user models can be generated for each tree-shaped data structure, which together form an overall user model assigned to the user.
  • Each tree-shaped data structure can be assigned a tree weighting.
  • the user model assigned to the user can be adapted. Elements referenced by the tree-shaped data structure can be inserted into the user model and treated as elements of the tree-shaped data structure.
  • a generated user model can be assigned information about the user model type.
  • the method may further include selecting objects based on predetermined selection criteria, wherein an object comprises a user model or an item model.
  • the selection criteria may include:
  • Objects having a predetermined similarity to the user model and / or item model similarity values being determined between the generated user model and / or item model and the objects before the selection.
  • a promotion program By an item model, a promotion program can be represented, wherein the item model representing the promotion program is selected when the user model has a predetermined similarity to the item model.
  • a processing device which is coupled to the storage device and which is adapted to carry out a method according to one of the preceding claims, in order to generate a user model and to store the generated user model in the storage device and make it available to a recommendation service.
  • a data carrier product is provided, with a program code stored thereon, which can be loaded into a computer and / or into a computer network and is adapted to carry out a method according to the invention.
  • Fig. 3 is a so-called “Collaborative Filtering” method, as it is known from
  • FIGS. 6a, 6b show two tree-shaped data structures which have the same meaning in the sense of the invention; and FIGS. 7a, 7b show a flow diagram of a method according to the invention. Detailed description of the invention
  • a tree-shaped data structure (hereinafter BD) is a data structure with which a monohaurarchy can be mapped.
  • nodes in the data structure are connected in a tree-shaped manner by means of edges.
  • Each child node can in turn have any number of children's nodes.
  • Examples of BD within the meaning of the invention are, but are not limited to, directory structures and / or file systems on a hard disk (folders and files) or so-called mind maps. If the BD is a file system, the "leaves" (which are the last nodes of a path in a BD, respectively) correspond to files or file associations, and all other nodes correspond to directories or folders. Nodes of a BD usually contain one or more elements. These elements can be of different types.
  • Common elements or element types are: text (in the case of a file system, the node text would be the file or directory name), additional notes, tables, appointments, multimedia objects (music, film, image), icons, formulas, links (usually to external Items), numbers, and / or binary code (especially if the BD is a directory structure and the node is a file).
  • a reference can be a unique URI (Uniform Resource Identifier), eg hyperlink, local link / link to a file on a storage medium (eg hard disk).
  • a reference can also be a non-unique description that identifies an item (eg title of a document, author name, photo, BibTeX Key, name of a place or product).
  • Each of the elements can have a number of attributes.
  • nodes themselves can also have attributes, in particular to format the display of the nodes or to assign specific functions to the node.
  • nodes may be represented as "collapsed” or “expanded” by attributes, that is, visible or invisible to the user.
  • attributes that is, visible or invisible to the user.
  • individual elements of a node can be visible or invisible to the user.
  • Edges in a BD are usually undirected and usually contain no textual information. Edges can also be directed.
  • Item items are arbitrary objects, ie, for example, documents (books, web pages, scientific articles), files, advertisements (in image, text, sound), persons, music pieces or music albums, movies, products, geographical locations, etc. or theirs Digital representation (ie not necessarily a physical book, but eg the digital copy / representation of the book in various formats).
  • a user is a person who applies or uses the system according to the invention.
  • a user can also be a so-called agent, a type of electronic person or a system that simulates the behavior of a real person.
  • User Model - A user model includes the interests, knowledge or other information about the person, usually in machine-readable form.
  • interests or knowledge of a user or information about the user are used synonymously.
  • - Connection between BD and user - A BD is in connection with a user or is assignable to the user if this user created, edited, downloaded or opened the BD, for example, or if the BD was or was in the possession of the user (eg is stored on the user's hard disk).
  • a collection is the set of all BD to which the system according to the invention has access.
  • tree-shaped data structures BD which are associated with the user or can be assigned to a user are analyzed in order to obtain a model of the user, i. a user model to create.
  • a user model includes, but is not limited to, information about the user's interests and knowledge.
  • a tree-shaped data structure BD comprises a number of nodes, wherein a special node represents the root node or the root node.
  • the other nodes are referred to as children's nodes, where the children's nodes are connected via edges to the root node or to a child node. Nodes that do not contain child nodes are called "leaves".
  • Each node may contain one or more references to external items. In Fig. 5, the node 2.i has such a reference to an item.
  • the content of a tree-shaped data structure and possibly the items which are linked from a tree-shaped data structure or the contents of the linked items describe the interests of the user and can be used to generate a user model.
  • FIGS. 7a and 7b show a flow diagram of a method according to the invention for generating a user model from at least one tree-shaped data structure.
  • a preprocessing takes place in which the tree-shaped data structures are adapted or processed for further processing.
  • the preprocessing step is an optional step and does not necessarily have to be performed, for example if the tree-shaped data structures already have the format required for further processing.
  • the preprocessing may include converting the tree-shaped data structures to a system-readable format. Further, preprocessing may involve deleting nodes from the tree-shaped data structures, i.e., deleting certain nodes that are not relevant to creating a user model. Deleting a node means that it is removed from the tree-shaped data structure and the child nodes of the node to be deleted are either also removed or the child nodes are assigned to the parent node of the node to be deleted. For example, a node may be dropped if the node meets one or more of the following criteria:
  • the node is empty
  • the node contains a specific element (not), such as text or reference; - The node has a certain attribute (not); - The node or elements of the node are not directly connected to the user. This may be the case if a node was not created by the user (eg nodes of a mind map linking to a file and where the text of the node is equal to the file name of the linked file, it can be assumed that the node is automatically, eg was created by "drag &drop", so the text of the node was not generated by the user, and therefore has little or no meaningfulness).
  • a node was not created by the user (eg nodes of a mind map linking to a file and where the text of the node is equal to the file name of the linked file, it can be assumed that the node is automatically, eg was created by "drag &drop", so the text of the node was not generated by the user, and therefore has little or no meaningfulness).
  • a user model is generated in a next step based on the tree-shaped data structure.
  • the nodes of a tree-shaped data structure or the elements of the nodes are analyzed in order to identify the interests etc. of the user and store them in a user model which is assigned to the user. This happens in the following steps:
  • the weighting of the nodes is based on the assumption that some nodes or their elements are more meaningful to describe the interests of the user than other nodes or their elements.
  • two partial weights can be calculated. However, it is also possible to calculate only one of the two partial weights and to consider this partial weight as the node weight of a node.
  • the two subweights include static node weighting and dynamic node weighting. Of course, other subweights not mentioned here can also be calculated.
  • the combination of the calculated part weights gives the node weight of a node. For static node weighting, the following criteria can be taken into account:
  • a node is weighted depending on the number of child nodes, e.g. the more children the knot has the more weight this knot receives.
  • Sibling nodes of a node are those nodes that have the same parent node as the considered node.
  • the node is weighted depending on the number of sibling nodes of the node, e.g. The more siblings a knot has, the less weight it gets.
  • Attributes If the node is highlighted by a particular attribute, e.g. by colored marking or underlining, he gets more weight. Is it weakened by certain attributes, e.g. by graying out or stroking it, it gets less weight.
  • the nodes can also be weighted dynamically, ie changes and usage intensity of the tree-shaped data structure over time can flow into the node weighting. If, for example, a node is used more intensively, or has been used more intensively in the past than other nodes, then this node can receive a higher weight than the other nodes.
  • the weighting can result among other things from: - Age of the knot: Older knots may receive more or less weight than younger knots. Preferably, younger knots get a higher weight.
  • Number of edits Preferably, a node that has been edited more often than other nodes may be given a higher weight.
  • - Number of shifts The more often a node has been moved (cut and reinserted), the more weight it receives.
  • Each of the above-mentioned (dynamic) weights may be weakened or enhanced by a time parameter.
  • a node is weighted twice as much if it has been edited at least twice. If the last edit is longer than X weeks, the weighting is weighted only 1.5 times by a damping time parameter.
  • inheritance of weights may be provided. Inheritance is preferably performed after the static or dynamic weighting has been performed.
  • nodes can "inherit" weights from their surrounding nodes. If, for example, a parent node has a very high weight (because, for example, he was often selected), the child node can also be given a higher weight than if it were only considered individually. Preferably, all children's nodes and their nodes, all sibling nodes and all parent nodes and their parents get a higher weight to the root, the additional weight getting weaker the farther away from the hereditary node is the inheriting node.
  • nodes that exceed a threshold value can inherit the weight to surrounding nodes.
  • a threshold value eg weighting five times greater than normal
  • the weighting of the individual nodes of the group can be matched or inherited.
  • Groups can be visually recognizable in the tree-shaped data structure or can be distinguished by specific attributes or element types. For example, a tree-shaped data structure contains some nodes that have references. All of these nodes are assigned to the Reference Node group. Although only 95% of these nodes have a very high weighting, the system assigns a very high weighting to all nodes (including the remaining 5%). The weak nodes of a group inherit from their other group nodes.
  • the determined element is a text element
  • this text is further decomposed into tokens or terms (terms).
  • the term can be a single word, but sometimes also compound words like "Mind Map".
  • each term is considered an independent element of type Text.
  • Latent Semantic Indexing combines or considers synonyms of words.
  • Translation the words are translated into a reference language, e.g. English, translated.
  • the determined element can also be a reference.
  • References can also be preprocessed by, for example, converting the URI (Uniform Resource Identifier) and / or the special characters to a uniform format for each reference or, if it is not a unique reference (eg only the title of a document) will find a unique identifier (in the case of a document, for example, the ISBN).
  • URI Uniform Resource Identifier
  • each element of a node can also be weighted. Especially text and reference elements are important for the creation of the user model according to the invention.
  • each element first receives a predetermined weighting (initial weighting), such as the weighting 1 or the weighting of its associated node. This can be done, for example, in an initialization step in which all elements are provided with an initial weighting.
  • the initial weight of an element may be strengthened or weakened, preferably based on one or more of the following factors:
  • Elements of certain types can receive different basis weights. For example, a text element representing the general node text may receive a higher weighting than a text element representing an additional note.
  • - Attributes Depending on the attributes, elements can get a stronger or weaker weighting. For example, a text element that is bolded may be weighted more heavily than a text element without formatting.
  • - BD frequency the less tree-shaped data structures in the entire collection have an element, the more it is weighted. This is based on the assumption that an element, which occurs only a few times in all tree-shaped data structures, is more meaningful than an element that occurs in almost every tree-shaped data structure. For example, in a collection of 100 tree-shaped data structures, if only a single tree-shaped data structure contains the term "tree," then this term would be weighted more heavily with respect to the tree-shaped data structure than if 90 other tree-shaped data structures also contained that term. Collection Frequency: The less often the element appears in the total of all elements of the entire collection, the more it is weighted. This is very similar to the BD frequency, except that the BD frequency counts the number of tree-shaped data structures in which the element occurs and, at its collection frequency, the total number of elements itself.
  • BD size The larger the tree-shaped data structure, the less heavily the element is weighted. This is based on the assumption that large tree-shaped data structures tend to contain more elements but should not be favored over small tree-shaped data structures.
  • the size of a tree-shaped data structure can be specified by the number of nodes of a tree-shaped data structure or by the number of elements in a tree-shaped data structure.
  • Position in the node Elements that are in the front of the node are weighted differently than elements further back in the node. If a node contains, for example, 100 terms, then it can be provided that only the first ten terms are taken into account. Furthermore, it can be provided that the further terms (for example the next 10 terms) are taken into account with less weight.
  • Language if the node contains text elements: Unlike documents, such as web pages, tree-shaped data structures often include terms in different languages. The elements of a node can be weighted differently depending on the language. This also means that if e.g. the text of a node in a particular language is weighting the other elements of the node (for example, a reference) less or more.
  • Node length Elements are weighted depending on the node length. The fewer elements a node contains, the more its elements can be weighted. - Distance to similar elements: The less similar elements in the vicinity of a node to which the element belongs, the more weight the element gets. For example: If a node has a reference to an item and the surrounding nodes (eg all children, siblings and parent nodes) do not contain any references, then this reference could be given a particularly high weight, as it seems reasonable to assume that the reference also refers to the surrounding nodes. If, on the other hand, sibling nodes also have references, this reference does not receive a particularly high weight.
  • Tree-shaped data structures can be created very user-specifically. For example, it may happen that a user
  • FIGS. 6a and 6b illustrate this case.
  • FIGS. 6a and 6b each show a tree-shaped data structure with the same statement of two users, the tree-shaped data structures nevertheless appearing differently.
  • the term "recommending” is repeated several times, but not in FIG. 6b.
  • the term “recommender” would be equally applicable for both tree-shaped data structures or users and should be equally weighted.
  • the weighting of the elements can also take place using the same methods with which the nodes are weighted. For example, older elements can be weighted less heavily than newer ones, and inheritance can also take place in element weights.
  • a user model it may be advantageous to store the generated user model to make it about a recommendation service for To make available.
  • a user model can also be created on demand without saving it.
  • At least two different approaches can be used to store a user model.
  • the two approaches shown here are type-neutral storage and type-dependent storage of a user model.
  • type-neutral storage all elements are stored with their weighting. That is, terms, links, links, images, etc. are all stored together in the model.
  • the vector space model described at the outset can be used for this purpose, which is extended by the invention such that not only terms with a weighting can be stored, but also any elements of different types with their weighting and their type.
  • a separate user model can be generated for each element type, which together form an overall user model.
  • a user model then includes, for example, a text model and a reference model.
  • standard methods from the information retrieval area or user modeling area can be used.
  • a standard model for a text-based model would again be the named vector space model in which the individual terms are weighted according to the method described above.
  • References can also be stored in other models that, for example, also take into account the order of the elements in the tree-shaped data structure.
  • the subsequent steps can optionally be carried out for the abovementioned steps.
  • one (or more) models can be generated for each tree-shaped data structure, as described above, and the various models are finally joined together to form an overall model.
  • different tree-shaped data structures can be provided with different weighting. The weighting follows similar principles as the weighting of the nodes or elements. For example, a newer tree-shaped data structure or tree-shaped data structures that are opened or edited more frequently may be weighted more heavily.
  • the existing user model can be extended by the elements of the new tree-shaped data structure.
  • a tree-shaped data structure contains references to items
  • these items can also be used to generate a user model. That is, the elements in the linked item are inserted into the user model in a manner similar to elements of the tree-shaped data structure itself. These items can be given a lower weighting.
  • the linked item is a tree-shaped data structure, its elements are weighted using the method described above. If the linked element is e.g. a web page, then the weighting can be done with standard methods, like the TF-IDF.
  • Models for short-term interests For example, this model would only contain data from a session or the last edited tree-shaped data structure (or data of the tree-shaped data structure that were edited in a certain period of time).
  • Long term interest models This model would contain interests based on all or at least several tree-shaped data structures.
  • Models for Different Interests It is conceivable that users create different tree-shaped data structures for e.g. different projects. That is, a tree-shaped data structure (or even several) are used for project A and another tree-shaped data structure (or more) for another project B. According to the invention, tree-shaped data structures that are very different can be used for creating different models (the possibly also be subdivided into long-term and short-term interests).
  • the identification of related tree-shaped data structures can be done as follows:
  • Tree-shaped data structures can be used for different types of applications, such as file management, brainstorming, document management, project planning, etc.
  • the type of application is noted in the user model. When a user creates different tree-shaped data structures for different types of applications, different user models are created again.
  • the type of application can be determined as follows:
  • the user can specify for which purpose he wants to create the tree-shaped data structure (for example brainstorming, project planning, etc.).
  • Automatic analysis the system analyzes the tree-shaped data structure and closes, e.g. on the basis of their structure, their use or their source format, automatically on the type of application.
  • the primary application is file management, web page management or document management.
  • the automatic analysis may include factors such as growth rate, size, duration of use, type of usage, application to create the tree-shaped data structure, and / or other factors.
  • each user model has been generated for a number of users.
  • These user models are used according to the invention to give a user recommendations for items. That is, based on the user models, items can be identified that the user is likely to consider interesting. sant / relevant.
  • a method is suggested which considers both item models and user models as equal.
  • the method according to the invention for proposing objects can comprise at least the following steps:
  • the user models have already been created and stored with the aforementioned methods according to the invention.
  • an item model is generated using techniques known in the art. For example, if the item is a web page, terms could be weighted using TF-IDF and saved as a Vector Space Model. If the item is a scientific paper, TF-IDF could be used, as well as other methods, such as Citation Proximity Analysis, to model the item.
  • the above type-neutral method can also be used to create a corresponding model of items which, like tree-shaped data structures, contain several element types.
  • a scientific paper usually contains text and references (references to other scientific papers), comparable to a tree-shaped data structure, which also contains text and references (eg to files). Therefore, both can be mapped relatively easily with compatible model types. It is important that according to the invention all objects are mapped in the same or a compatible model in order to be able to compare them later. Once models have been created and saved from all objects, they are compared for similarity. Do the object models contain several submissions? For example, for short-term and long-term interests or for different element types, each of these submodels is compared to the other object models.
  • the comparison can take place using standard methods, such as Cosine for comparisons in the Vector Space Model or similarity measures such as Greedy Citation Tiling for reference-based models.
  • a method based on machine learning can be used.
  • the references of a groove Zermodells can be used with machine learning techniques to learn user preferences.
  • any reference to an item is considered a positive association that the system learns and provides recommendations for new items based on it.
  • this can be used to make recommendations for subsidies / funding programs.
  • a user model is first generated from a tree-shaped data structure, as described above.
  • the support program itself is considered an item.
  • the item in turn is represented by a text describing the funding program.
  • This text can be a website, a brochure in PDF format, social tags, etc. If, for example, a user model contains the heavily weighted term "Recommender Systems" and there is a funding program whose website also often includes this term, this support program would be recommended to the user ,

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Abstract

L'invention concerne un procédé et un système servant à élaborer un modèle d'utilisateur, en particulier pour un service de recommandation, à partir d'au moins une structure de données arborescente. Le modèle d'utilisateur contient des informations relatives à un utilisateur, la ou les structures de données arborescentes pouvant être affectées à l'utilisateur. La structure de données arborescente comprend un nœud racine et une pluralité de nœuds enfants qui sont reliés par des bords au nœud racine ou à un nœud enfant. On affecte au moins un élément à un nœud et on détermine les éléments affectés aux nœuds, lesdits éléments représentant un contenu du nœud respectif. On pondère les éléments déterminés et on affecte une pondération élémentaire à chaque élément. On génère un modèle d'utilisateur, le modèle d'utilisateur généré comprenant les éléments déterminés et la pondération élémentaire affectée aux éléments respectifs.
PCT/EP2011/070873 2011-11-23 2011-11-23 Procédé et système d'élaboration de modèles d'utilisateurs Ceased WO2013075745A1 (fr)

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Publication number Priority date Publication date Assignee Title
CN110825478A (zh) * 2019-11-05 2020-02-21 广东优世联合控股集团股份有限公司 一种主界面内容添加方法、装置、介质和电子设备
CN111159503A (zh) * 2019-12-30 2020-05-15 广东三扬网络科技有限公司 基于思维导图的产品特征展示方法、电子设备和存储介质

Non-Patent Citations (1)

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Title
Die einzigen technischen Aspekte der beanspruchten Erfindung beziehen sich auf allgemein gebräuchliche Datenverarbeitungstechnologie. Aufgrund seiner weiten Verbreitung kann die allgemeine Bekanntheit eines solchen Standes der Technik nicht bezweifelt werden. Siehe Amtsblatt 11/2007, S. 592. *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110825478A (zh) * 2019-11-05 2020-02-21 广东优世联合控股集团股份有限公司 一种主界面内容添加方法、装置、介质和电子设备
CN111159503A (zh) * 2019-12-30 2020-05-15 广东三扬网络科技有限公司 基于思维导图的产品特征展示方法、电子设备和存储介质

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