Content-Based Top-N Recommendation Using Heterogeneous Relations
Published in The Twenty-Seventh Australasian Database Conference (ADC-16), 2016
Recommended citation: Yifan Chen, Xiang Zhao, Junjiao Gan, Junkai Ren, and Yanli Hu. The 27th Australasian Database Conference . ADC 2016.Springer. Best Paper Award
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 recom-mender system by learning the global term weights in profiles. To achieve this, we bring in PathSim, which could well measures 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.