Abstract
Top-N recommender systems have been extensively studied. However, the sparsity of user-item activities has not been well resolved. While many hybrid systems were proposed to address the cold-start problem, the profile information has not been sufficiently leveraged. Furthermore, the heterogeneity of profiles between users and items intensifies the challenge. In this paper, we propose a content-based top-N recommender system by learning the global term weights in profiles. To achieve this, we bring in PathSim, which could well measure the node similarity with heterogeneous relations (between users and items). Starting from the original TF-IDF value, the global term weights gradually converge, and eventually reflect both profile and activity information. To facilitate training, the derivative is reformulated into matrix form, which could easily be paralleled. We conduct extensive experiments, which demonstrate the superiority of the proposed method.
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Notes
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Without loss of generality, it is straightforward to extend the idea to the case of auxiliary information on both sides of users and items.
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Acknowledgment
X. Zhao was partially supported by NSFC 61402494 and NSF Hunan 2015JJ4009; Y. Hu was partially supported by NSFC 61302144.
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Chen, Y., Zhao, X., Gan, J., Ren, J., Hu, Y. (2016). Content-Based Top-N Recommendation Using Heterogeneous Relations. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_24
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