Kernel based Collaborative Topic Regression for Tag Recommendation

Tag recommendation is very helpful for users to organize or categorize online resources like music, photos and articles. In recent years, some models, such as collaborative topic regression (CTR) and its variants, have demonstrated promising performance for tag recommendation. In this paper, we propose a novel Bayesian model, called Kernel based CTR (KCTR) to combine kernel based probabilistic matrix factorization which exploits social networks with topic modeling. In contrast to CTR and its existing variants, KCTR model is capable of keeping the real correlation among items rather than ideally assuming the relations among items are mutually independent, which is hardly satisfied in real world. Experimental results on two real datasets show that our method outperforms the state-of-the-art approaches.