Abstract
The category of the product acts as the label of the product. It also exemplifies users various needs and tastes. In the existing recommender systems, the focus is on similar products recommendation with little or no intention to investigate the cross-category and the complementary relationship between categories and products. In this paper, a novel method based on Bayesian Personalized Ranking (BPR) is proposed to integrate the complementary information between categories and the latent features of both users and items for better recommendation. By considering category dimensions explicitly, the model can alleviate the cold start issue and give the recommendation not only considering traditional similarity measure but complementary relationships between products as well. The method is evaluated comprehensively and the experimental results illustrate that our work optimized ranking significantly (with high recommendation performance).
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Acknowledgments
This work is supported by NSFC (No. 61170192) and the Fundamental Research Funds for the Central University for Student Program (XDJK2017D059 and XDJK2017D060).
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Yu, W., Li, L., Hu, F., Li, F., Zhang, J. (2017). Modeling Complementary Relationships of Cross-Category Products for Personal Ranking. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10570. Springer, Cham. https://doi.org/10.1007/978-3-319-68786-5_8
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DOI: https://doi.org/10.1007/978-3-319-68786-5_8
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