Abstract
With the popularity of online social networks, social network information is becoming increasingly important to improve recommendation effectiveness of the existing recommender systems. In this paper, we propose an improved trust-aware recommendation approach, called TRA. TRA constructs a new social trust matrix based on users’ trust relationships derived from online social networks to alleviate the problem of data sparsity, and meanwhile naturally fuses users’ preferences and their trusted friends’ favors together by means of probability matrix factorization. The experimental results show that TRA performs much better than the state-of-the art recommendation approaches.
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Acknowledgment
This work is supported by the National Nature Science Foundation of China (61170174), Major Research Project of National Nature Science Foundation of China (91646117), Natural Science Foundation of Tianjin (17JCYBJC15200) and Tianjin Science and Technology Correspondent Project (16JCTPJC53600).
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Xiao, Y., Bu, Z., Hsu, CH., Zhu, W., Shen, Y. (2017). Trust-Aware Recommendation in Social Networks. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_32
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DOI: https://doi.org/10.1007/978-3-319-63558-3_32
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