Recommender System Using Implicit Social Information

Li, Yusheng; Song, Meina; Haihong, E

doi:10.1587/transinf.2014edp7174

Cited by 6 publications

(9 citation statements)

References 22 publications

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“…As shown in Figure 11, it can be concluded that the recommendation accuracy can achieve its better performance when = 0.4; i.e., the trust relationship and the similarity reach a balance on the Epinions and Tencent datasets, respectively. The regularization constant of user feature in [18][19][20][21][22].…”

Section: Results Of the Experimentsmentioning

confidence: 99%

“…where denotes the parameter of time attenuation and t ui and t vi denote the times of the item i rated by users u and v, respectively. The improved similarity between user u and user v is described as follows (see (22)):…”

Section: Definition 2 Average Similarity Standard Deviation (Assd)mentioning

confidence: 99%

“…However, when some users have few or no ratings on items, it is difficult to predict unknown ratings according to nearest neighbors and the ratings obtained are inaccurate. In this section, inspired by [13,18,19,21,22,46,51], an enhanced social matrix factorization model fusing user's social status and item's similarity is proposed, which is called ESMF, to predict all unrated items. The graphical model is shown in Figure 6.…”

Section: Complete Matrix With Matrix Factorization Algorithmmentioning

confidence: 99%

“…In the experiments, the proposed method (GMMESMF) is compared to the probabilistic matrix factorization (PMF) [34], reliabilitybased trust-aware collaborative filtering (RTCF) [24], matrix factorization-based model for recommendation in social rating networks (SocialMF) [18], time-and community-aware RS (TCARS) [49], context-aware recommender system via individual trust among users (CSIT) [19], imputation-based matrix factorization (IMF) [23], and implicit social recommendation (ISRec) [22] in terms of the MAE, P@N, and R@N measures on the Epinions and Tencent datasets. PMF is proposed by Salakhutdinov and only uses the user-item rating matrix for recommendations based on probabilistic matrix factorization.…”

Section: Performance Comparison and Analysismentioning

confidence: 99%

“…The tradeoff parameter plays the role of adjusting the effects of interpersonal trust between users in [21,22].…”

mentioning

confidence: 99%

See 4 more Smart Citations

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

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Hua

Gao

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Mathematical Problems in Engineering

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Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items. In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent. However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix. Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation. To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology. Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted. Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity. Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group. Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings. Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models. Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in accuracy.

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