Abstract
Collaborative filtering (CF) technique plays an important role in generating personalized recommendations, but its performance is challenged by the problems of data sparsity and cold start. Besides, different CF methods have their own advantages, so another tough issue is how to exploit the complementary properties of different methods. In this paper, we propose a general framework to ensemble three popularly used CF methods, termed as TriCF, aiming to further elevate recommendation accuracy. In order to alleviate the data sparsity problem, we incorporate social information into TriCF by graph embedding, denoted as SoTriCF. In particular, a mapping from social domain to rating domain is built by a neural network model, which can enhance the cold-start users’ latent representation learned from rating data. Extensive experiments on three real-world datasets show that the proposed approaches achieve significant improvements over state-of-the-art methods.
This work was performed when the second author was a joint-training student of Beijing University of Civil Engineering and Architecture (BUCEA) and Beijing Jiaotong University (BJTU) from 2015 to 2018.
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Acknowledgments
The authors would like to thank the anonymous reviewers for their constructive suggestions. This work was supported in part by the ‘Natural Science Foundation of China’ #61671048 and the ‘Research Foundation for Talents of Beijing Jiaotong University’ #2015RC008.
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Zhang, H., Liu, G., Wu, J. (2018). Social Collaborative Filtering Ensemble. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_77
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