  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.
