Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation
This work addresses the problem of improving recommendation accuracy for users in social web applications, though it appears incremental as it builds on existing matrix factorization and deep-semantic techniques.
The paper tackles the cold start problem in tag-aware personalized recommendation by proposing a hybrid deep-semantic matrix factorization model, which significantly outperforms state-of-the-art baselines with a mean reciprocal rank 1.52 times higher and mean average precision 1.66 times higher.
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.