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Collaborative filtering method based on coupling topic model

A topic model and topic technology, applied in the field of information recommendation of Internet products, can solve problems such as poor interpretability of feature vectors, and achieve the effect of solving sparse problems

Active Publication Date: 2014-07-02
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

However, the traditional matrix decomposition method only considers the analysis of historical scoring information, and does not mine the information related to the text content, so the interpretability of the learned feature vector is poor.

Method used

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  • Collaborative filtering method based on coupling topic model

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Embodiment Construction

[0017] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0018] The present invention proposes a coupled topic model based on matrix decomposition and topic model. By mapping users and products to the hidden topic space, learn a K-dimensional feature vector η for each user and product, and replace Dirichlet prior by introducing logistic normal prior, so that the topic vector θ( While K is the number of topic vectors), a more flexible feature vector η can be learned, which is no longer limited to the corresponding simplex (a K-dimensional vector θ satisfies Then it is said that the vector is distributed on the simplicity of K-1), which not only makes the eigenvector more expressive, but also makes it more flexible to use matrix decomposition for scoring prediction.

[0019] ...

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Abstract

The invention discloses a collaborative filtering method based on a coupling topic model. The collaborative filtering method based on the coupling topic model is used for effectively combining historical grading information and user-generated content which is UGC in short in a recommendation system and performing effective recommendation through grading prediction. The method includes the following steps that firstly, a record of the user-generated content is obtained; secondly, a grading record on a product by the user is obtained; thirdly, a user file and a product file are extracted; fourthly, the coupling topic model is used for learning user feature vectors and product feature vectors; fifthly, the grade on different products by a target user is calculated according to the feature vectors, and corresponding product recommendation is performed. The analysis on user-generated content information is introduced into the collaborative filtering method, the user interest and product properties can be directly and explicitly found, the sparse problem of a grading matrix is effectively solved, and the effect more accurate than the prediction based on user grading information is obtained.

Description

technical field [0001] The present invention relates to the field of information recommendation of Internet products, especially for the simultaneous existence of user-generated content information and user rating information in the website system, how to effectively use user-generated content and combine historical rating information at the same time, accurately analyze user preferences and product attributes, and provide target users Make personalized information product recommendations. Background technique [0002] With the in-depth development of Internet technology and web2.0, user-generated content (User-generated Content, referred to as UGC) has gradually become a new type of mainstream network information resources. User-generated content generally refers to text, pictures, audio, video and other content created by users published on the Internet in any form. This invention mainly analyzes the user-generated content of the recommendation system, that is, the UGC of ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06F17/30
Inventor 王亮吴书徐松
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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