Abstract
As for the data sparsity and cool boot problem, this paper brings forward a social network recommendation means combined with social tags and trust relations. It collects major information relating to social trust relations, item tag information and user rating matrix based on probabilistic matrix factorization. All the data resources from different dimensions are connected through shared users potential spaces (or item potential spaces). The above mentioned two types of spaces can be obtained by probabilistic matrix factorization.In this way, effective social recommendation means can be achieved. The results generated from Epinions and Movielens experiments reveal that the proposed algorithm is superior to the existing Trust-based Social Recommendation or Social Tag Recommendation especially for active users with only a few rating records.
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Acknowledgements
The research is supported by the Social development project of Lianyungang City, No. (SH1507). The research is supported by the top-notch Academic Programs Project of Jiangsu Higher Education Institution (PPZY2015a038), National Natural Science Funds of China (Grant Nos. 61403156, 61403155), the Prospective Joint Research of University-Industry Cooperation of Jiangsu (No. BY2016056-02). The Lianyungang Science and Technology Project under Grant CK1503, CXY1530, CG1611. The Science and Technology project of Jiangsu Province under Grant BN2016065.
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Li, H., Zhang, S., Hu, Y. et al. Research of social recommendation based on social tag and trust relation. Cluster Comput 21, 933–943 (2018). https://doi.org/10.1007/s10586-017-0962-9
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DOI: https://doi.org/10.1007/s10586-017-0962-9