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A product recommendation method based on tucker decomposition and knowledge graph

A knowledge graph and product recommendation technology, applied in business, instrumentation, data processing applications, etc., can solve the problems of missing information, inability to recommend system learning, simple knowledge graph completion model, etc., to achieve the effect of improving the accuracy of recommendation

Active Publication Date: 2022-08-02
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

Some existing models that combine knowledge graph completion tasks and recommendation system tasks have some problems. The main reason is that the knowledge graph completion model used is too simple, and the representation of entities and relationships in the knowledge graph learned by this method will be missing. Some information, and the inner product between vectors is often used in the recommendation module to calculate the recommendation score of the product for the user. This simple method cannot fully enable the recommendation system to learn the representation of users and products.

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  • A product recommendation method based on tucker decomposition and knowledge graph
  • A product recommendation method based on tucker decomposition and knowledge graph
  • A product recommendation method based on tucker decomposition and knowledge graph

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Embodiment 1

[0034] This embodiment proposes a product recommendation method based on Tucker decomposition and knowledge graph. figure 1 The shown model is realized by a recommendation system module and a knowledge graph module, and the recommendation system module includes a product feature vector generation module and a preference feature vector generation module.

[0035] The product recommendation method includes the following steps:

[0036] Step 1: Build a recommendation learning database based on the interaction records between users and products in the past, in which the interaction method can be that a user has clicked on a product; build a number of ternaries in the form of . A knowledge graph database consisting of a group and containing the information of all commodities in the recommendation learning database, in which the head entity and the tail entity belong to entities; since the actual meaning of the only entity corresponding to the commodity in the knowledge graph databa...

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Abstract

The invention discloses a product recommendation method based on Tucker decomposition and knowledge graph, which belongs to the technical field of information recommendation. First, a recommendation learning database, a knowledge graph database and a contact data table are constructed; and feature vectors of each user, product, entity and relationship are obtained by initialization; Then calculate and obtain the product feature vector and preference feature vector respectively, combine the user feature vector and use the inverse operation of Tucker decomposition to learn the recommendation system information, extract the triplet and use the inverse operation of Tucker decomposition to learn the knowledge map information, and obtain the corresponding score function respectively; The recommendation learning database and the knowledge graph database are trained as the training set, and the trained model is used to recommend top-N products to users. The invention performs joint learning on the knowledge map completion task and the recommendation system task, and uses the inverse operation of Tucker decomposition to calculate the recommendation system score, thereby improving the recommendation result.

Description

technical field [0001] The invention belongs to the technical field of information recommendation, and in particular relates to a product recommendation method based on Tucker decomposition and knowledge graph. Background technique [0002] At present, it has gradually become a trend to introduce knowledge graphs into recommender systems. The rich entity information and relationship information in the knowledge graph can help the recommender system to understand the relationship between users and products at a deeper level, thereby providing more accurate recommendation results. [0003] A knowledge graph is essentially a collection of triples, where the triples are of the form <head entity, relation, tail entity>. Each triplet represents a fact in real life, for example the triplet <Joss Whedon, director of, The Avengers> represents the fact that "Joss Whedon is the director of the Avengers". The knowledge graph completion task is an important task in the fiel...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06G06F16/2458
CPCG06Q30/0631G06F16/2458
Inventor 曹扬杨波李少松
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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