Probability matrix decomposition recommendation method based on user context coupling similarity

Abstract

The invention discloses a probability matrix decomposition recommendation method based on user context coupling similarity. The method comprises the steps of firstly, constructing a user relationshipmatrix in combination with the coupling similarity of user context information and user credibility; combining the user trust relationship matrix and the user project matrix to carry out probability decomposition to learn a feature vector; and finally finishing recommendation through trusted users of new users. The invention aims to provide the probability matrix decomposition recommendation method based on user context coupling similarity. The user context information coupling similarity serves as a special trust relationship, a user trust relationship matrix is constructed through the coupling similarity between the user context information and the user trust degree, and then potential feature vectors are learned through probability decomposition in combination with a user scoring matrix, so that recommendation is achieved. The problems of cold start and sparsity which widely occur in a recommendation system are solved.

Description

technical field [0001] The invention relates to the field of user context recommendation, in particular to a probabilistic matrix decomposition recommendation method based on user context coupling similarity. Background technique [0002] With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, various services and user data on the Internet have exploded. These big data contain rich value and great potential, which has brought transformative development to human society. How to quickly and effectively obtain valuable information from complex data to make personalized recommendations for users is the research of recommendation system. key problem. Personalized recommendation system has become a hot spot in academia and industry and has produced many related research results. The recommendation system is based on user preferences, interests, etc., through recommendation algorithms to mine items of interest to users (such as in...

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Problems solved by technology

Document 2 (Gao Yukai, Wang Xinhua, Guo Lei, et al. A user cold-start recommendation algorithm based on collaborative matrix decomposition [J]. Computer Research and Development, 2017 (08): 188-198.) proposed a multi-layer The collaborative probability matrix decomposition algorithm combines the three-dimensional information of the user rating matrix, user type matrix and user business matrix to carry out probability decomposition to learn the potential characteristics of users, which improves the accuracy of recommendation results and alleviates the problem of user cold start, but ignores the relationship between user context relationship and the algorithm complexity is relatively high

Document 3 (Yao Yunfeng, Chen Lianna, YAOYunfeng, etal. Probabilistic matrix decomposition recommendation integrated into contextual relationship [J]. Journal of China Institute of Metrology, 2016, 27(3): 338-344.) User context similarity and probability matrix decomposition Model combination, although the contextual relationship is integrated into the probability matrix decomposition, it does not take into account the coupling relationship between contexts

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