Abstract
Recommender Systems based on collaborative filtering could provide users with accurate recommendation. However, sometimes due to data sparsity and cold start of the input ratings matrix, this method could not find similar users accurately. In the past, researchers used implicit trust weight instead of the similarity weight to find similar users, to improve the quality of recommendation [17]. And they often ignore the role of explicit trust in the process of finding similar users. Therefore, in this paper, we explore the calculation of implicit trust and explicit trust. Then according to their role in the recommendation system, we propose a method that combined trust and similarity to get a better recommendation. At last, by experimenting on FilmTrust [5] data set which has the explicit trust matrix, the result showed that the method we proposed significantly improve the quality of recommendation, in addition, implicit trust and explicit trust have a positive effect on the quality of the results of recommendation.
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (No. 61472021), SKLSDE project under Grant No. SKLSDE- 2015ZX-17 and the Fundamental Research Funds for the Central Universities.
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Ouyang, Y., Zhang, J., Xie, W., Rong, W., Xiong, Z. (2016). Implicit and Explicit Trust in Collaborative Filtering. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_39
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DOI: https://doi.org/10.1007/978-3-319-47650-6_39
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