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

Published in The 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (CCF Rank B), 2020

Authors: Zhenghua Xu*,Di Yuan, Thomas Lukasiewicz, Cheng Chen, Yishu Miao and Guizhi Xu
Abstract: 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.
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