Personalized Recommendation of Classification based on Social Relationship and Time Information

Improving recommender performance is beneficial. However, sparsity and scalability of data is a problem faced by recent recommender systems. Customer interests and available products are changing constantly. Social interactions among users are highly influential on the effectiveness of the recommendation. Following these intuitions, this paper proposed a recommendation method incorporating with social and temporal information with probabilistic matrix factorization, which is called PMFST (Probabilistic Matrix Factorization with Social and Temporal information), to solve the problem of data sparsity and achieve real and dynamic recommender performance. The experiment on two real data sets shows that the proposed method outperforms the state-of-the-art methods in terms of minimal error and recommender performance. Introduction In recommender systems, sparsity and scalability are two common difficulties. Much work addresses the challenge of cold start problem by using social relationship. People usually turn to their friendship/neighbors for help to obtain our needed information in real life. Massa et al. [1][2] proposed a CF frame based on trust and a trust degree algorithm—MoleTrust. In fact, people’s choices are influenced by not only social relationship but also time factor. The popularity of products on Internet may change with time, as well as people’s interests. Time information is crucial, especially when comments and ratings are related to products within a specific period to model recommendation. Koren et al. [3] study Netflix’s dynamic properties and succeed in enhancing predicting ratings with time information. However, few attempts have taken full advantages of social relationship and time information in recommender systems. In this paper, a probabilistic graph model is proposed to combine social relationship and time to achieve precise and comprehensive recommendation. The probabilistic graph model shows superior performance of inference. It achieves better performance than the state-of-the-art models. In recommender systems, large data sets are hard to handle due to limited resources. To illustrate the problem, Fig.1 describes our approach by exploiting part data of Epinions. We recommend items for users from rating (or other feedback) data concerning items that can be classified into different categories. It is our basic idea to transform recommending items into recommending categories firstly. Then we consider recommending item for every user according to the category that belongs to, which can effectively avoid computing large data sets. In this paper, we try to conduct a systematic research of improving recommendation performance with following contributions. We depend on tensor factorization model incorporating social and time information (PMFST) to realize real-time and dynamic recommendation, which overcomes the cold start and sparsity problem. We take some methods to recommend items according to the corresponding category, which realize particular and personal recommendation. We conduct our experiments on two different data sets: Epinions and Ciao. Experimental results indicate different data sets applied with the PMFST model, which can achieve good performance. 4th International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2016) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 71