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EDMF: Efficient Deep Matrix Factorization with Review Feature Learning for Industrial Recommender System - 2021

Edmf: Efficient Deep Matrix Factorization With Review Feature Learning For Industrial Recommender System

Research Area:  Machine Learning


Recommendation accuracy is a fundamental problem in the quality of the recommendation system. In this paper, we propose an efficient deep matrix factorization with review feature learning for the industrial recommender system (EDMF). Two characteristics in users review are revealed. First, interactivity between the user and the item, which can also be considered as the formers scoring behavior on the latter, is exploited in a review. Second, the review is only a partial description of the users preferences for the item, which is revealed as the sparsity property. Specifically, in the first characteristic, EDMF extracts the interactive features of onefold review by convolutional neural networks with word attention mechanism. Subsequently, L0 norm is leveraged to constrain the review considering that the review information is a sparse feature, which is the second characteristic. Furthermore, the loss function is constructed by maximum a posteriori estimation theory, where the interactivity and sparsity property are converted as two prior probability functions. Finally, the alternative minimization algorithm is introduced to optimize the loss functions. Experimental results on several datasets demonstrate that the proposed methods, which show good industrial conversion application prospects, outperform the state-of-the-art methods in terms of effectiveness and efficiency.


Author(s) Name:  Hai Liu; Chao Zheng; Duantengchuan Li; Xiaoxuan Shen; Ke Lin; Jiazhang Wang; Zhen Zhang; Zhaoli Zhang; Neal N. Xiong

Journal name:   IEEE Transactions on Industrial Informatics ( Early Access )

Conferrence name:  

Publisher name:  IEEE

DOI:   10.1109/TII.2021.3128240

Volume Information:   Page(s): 1 - 1