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

Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation