  Matrix factorization has now become a dominant solution for personalized
recommendation on the Social Web. To alleviate the cold start problem, previous
approaches have incorporated various additional sources of information into
traditional matrix factorization models. These upgraded models, however,
achieve only "marginal" enhancements on the performance of personalized
recommendation. Therefore, inspired by the recent development of deep-semantic
modeling, we propose a hybrid deep-semantic matrix factorization (HDMF) model
to further improve the performance of tag-aware personalized recommendation by
integrating the techniques of deep-semantic modeling, hybrid learning, and
matrix factorization. Experimental results show that HDMF significantly
outperforms the state-of-the-art baselines in tag-aware personalized
recommendation, in terms of all evaluation metrics, e.g., its mean reciprocal
rank (resp., mean average precision) is 1.52 (resp., 1.66) times as high as
that of the best baseline.
