Cross-domain recommendation method based on multi-view knowledge representation

A technology of knowledge representation and recommendation method, applied in the field of big data, it can solve the problems of insufficient consideration of the model, indistinguishable, inefficient computing power and space performance, etc., to achieve the effect of solving data sparse and cold start, and improving the recommendation performance

Pending Publication Date: 2021-03-23
BEIJING JIAOTONG UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

[0014] The disadvantages of the above-mentioned cross-domain recommendation schemes in the prior art are: most of them directly apply the collaborative filtering algorithm to cross-domain scoring data, or apply transfer learning to cross-domain recommendation, or use knowledge aggregation, which involves The knowledge mostly adopts the scoring matrix between users and items
However, in addition to the directly expressed rating data between user items, there are also fine-grained attribute preference associations between users and items. The existing models do not fully consider how to describe the fine-grained attribute characteristics and attributes of items in multiple fields. Relationships between items to learn item representations
[0015] Heterogeneous Information Networks (HIN) can represent directed graphs with multiple types of nodes and edges and various complex relationships. Most of the existing heterogeneous graph learning methods are non-end-to-end learning methods, which rely on The cumbersome process requires the use of random walk methods to manually select meta-paths or meta-graphs, or to perform knowledge representation construction, similarity, or off-line calculation of exchange matrices in heterogeneous graphs. inefficient
[0016] In the existing heterogeneous graph algorithm, the influence of different item attributes (views) in the heterogeneous graph data on the user's preference is not distinguished, but in reality, for different movies, users like to decide what to watch according to different movie attributes. Content, such as director, actor, and genre, have different influences on whether a user watches a movie

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Embodiment Construction

[0046] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0047] Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components, and / or groups thereof. It will be understoo...

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Abstract

The invention provides a cross-domain recommendation method based on multi-view knowledge representation. The method comprises the steps of integrating different projects in a heterogeneous graph formaccording to similar attributes of the projects in different fields to form a plurality of views, taking the views as inputs of a graph attention network respectively, and obtaining initial knowledgerepresentation of the projects under the views through the graph attention network; taking the initial knowledge representation of the project under each view as the input of a multi-head attention network, obtaining and integrating project representation vectors with user preferences under different views through the multi-head attention network, and obtaining the final representation of the project with the user preferences; and recommending a corresponding project in the target domain to the user according to the final representation of the project with the user preference and the information of the target domain. The multi-view multi-head attention network learning method is set among multiple fields, project knowledge representation is fully learned, cross-field recommendation is carried out, and therefore the recommendation effect of the target field is improved.

Description

technical field [0001] The invention relates to the field of big data technology, in particular to a cross-domain recommendation method based on multi-view knowledge representation. Background technique [0002] Due to the exponential growth of the amount of information available on the web, recommender systems have become inevitable in our daily lives. However, when users have less feedback on items or new users and items are added to the system due to lack of historical information, the recommendation performance will be extremely degraded, which is the so-called single-domain data sparsity problem and cold start problem. [0003] A natural solution to the above-mentioned problems in single-domain recommendation is to use data information from other domains to enrich user models to generate better recommendations, that is, cross-domain recommendations. For example, the genres of movies a user likes can be derived from the genres of books he likes. Furthermore, cross-doma...

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Application Information

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IPC IPC(8): G06F16/9536G06N3/08G06N20/00
CPCG06F16/9536G06N3/08G06N20/00Y02D10/00
Inventor 刘真杨禹辉王晓东张艳玲
Owner BEIJING JIAOTONG UNIV
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