Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel

Personalized tag recommendation systems recommend a list of tags to a user when he is about to annotate an item. It exploits the individual preference and the characteristic of the items. Tensor factorization tech-niques have been applied to many applications, such as tag recommendation. Models based on Tucker De-composition can achieve good performance but require a lot of computation power. On the other hand, mod-els based on Canonical Decomposition can run in linear time and are more feasible for online recommendation. In this paper, we propose a novel method for personal-ized tag recommendation, which can be considered as a nonlinear extension of Canonical Decomposition. Dif-ferent from linear tensor factorization, we exploit Gaus-sian radial basis function to increase the model’s ca-pacity. The experimental results show that our proposed method outperforms the state-of-the-art methods for tag recommendation on real datasets and perform well even with a small number of features, which verifies that our models can make better use of features