Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation

Algorithms

Lamei Pan¹  Zhen Tian²  Pu Yin²  Rui Wang² 

[1] School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;Shunde Graduate School, University of Science and Technology Beijing, Foshan 528300, China;

关键词: attention mechanism;    cross-features;    deep neural network;    information fusion;    matrix factorization;    recommendation system;   

DOI  :  10.3390/a14100281

来源: DOAJ

【 摘 要 】

The emergence of the recommendation system has effectively alleviated the information overload problem. However, traditional recommendation systems either ignore the rich attribute information of users and items, such as the user’s social-demographic features, the item’s content features, etc., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which introduces the attribute information and adopts the element-wise product between the different information domains to learn the cross-features when conducting information fusion. In addition, the attention mechanism is utilized to distinguish the importance of different cross-features on prediction results. In addition, the IFDNAMF adopts the deep neural network to learn the high-order interaction between users and items. Meanwhile, we conduct extensive experiments on two datasets: MovieLens and Book-crossing, and demonstrate the feasibility and effectiveness of the model.

【 授权许可】

Unknown