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 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.
DONATE to arXiv: One hundred percent of your contribution will fund improvements and new initiatives to benefit arXiv's global scientific community. Please join the Simons Foundation and our generous member organizations and research labs in supporting arXiv. https://goo.gl/QIgRpr
from cs.AI updates on arXiv.org http://ift.tt/28Zlfec
via IFTTT
No comments:
Post a Comment