Abstract
Collaborative filtering has become the most popular technique in the field of recommender system to deal with the information overload problem. Most collaborative filtering approaches either based on the intuitive nearest neighbor methods or the scalable latent factor models. In order to benefit from the advantages of these two paradigms, some hybrid strategies are proposed by taking weighted averages on near neighbors’ ratings as effects, or factorizing neighborhood to model interactions and relationships directly. However, these methods usually assume that the latent factors of users/items are independent of each other. Yet in fact, there are relationships among latent factors would affect the performance of recommendations. Motivated by this, in this paper, we introduce the collaborative factors, which are smoothed by near neighbors’ factors, to better capture the intrinsic features for users and items. We further propose a novel collaborative matrix factorization (CoMF) model in order to elaborately incorporate these collaborative factors into latent factor models. Finally, experimental results on two datasets show that our CoMF significantly outperforms some state-of-the-art methods in prediction accuracy.
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Adomavicius, G., Tuzhilin, A.: Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE TKDE 17(6), 734–749 (2005)
Bell, R., Koren, Y.: Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. In: IEEE ICDM, pp. 43–52 (2007)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative Filtering for Implicit Feedback Datasets. In: IEEE ICDM, pp. 263–272 (2008)
Koren, Y.: Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model. In: ACM SIGKDD, pp. 426–434 (2008)
Koren, Y.: Factor in the Neighbors: Scalable and Accurate Collaborative Filtering. TKDD 4(1), 1 (2010)
Ma, H., King, I., Lyu, M.R.: Learning to Recommend with Social Trust Ensemble. In: ACM SIGIR, pp. 203–210 (2009)
Ma, H., King, I., Lyu, M.R.: Learning to Recommend with Explicit and Implicit Social Relations. ACM TIST 2(3), 29 (2011)
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop, pp. 5–8 (2007)
Paolo, M., Avesani, P.: Trust-aware Bootstrapping of Recommender Systems. In: Workshop on Recommender Systems, pp. 29–33 (2006)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: ACM WWW, pp. 285–295 (2001)
Takács, G., Pilászy, I., Németh, B., Tikk, D.: A unified approach of factor models and neighbor based methods for large recommender systems. In: IEEE Applications of Digital Information and Web Technologies (ICADIWT), pp. 186–191 (2008)
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Yu, P., Lin, L., Wang, F., Wang, J., Wang, M. (2014). Improving Recommendations with Collaborative Factors. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_4
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DOI: https://doi.org/10.1007/978-3-319-08010-9_4
Publisher Name: Springer, Cham
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