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A hybrid neural network approach to combine textual information and rating information for item recommendation - 2021

Research Area:  Machine Learning


Collaborative filtering (CF) is a common method used by many recommender systems. Traditional CF algorithms exploit users ratings as the sole information source to learn user preferences. However, ratings usually sparse cause a serious impact on the recommendation results. Most existing CF algorithms use ratings and textual information to alleviate the sparsity of data and then utilize matrix factorization to achieve the latent feature interactions for rating prediction. Nevertheless, the following shortcomings remain in these studies: (1) The word orders and surrounding words of the textual information are ignored. (2) The nonlinearity of feature interactions is seldom exploited. Therefore, we propose a novel hybrid neural network to combine textual information and rating (NCTR) information for item recommendation. The proposed NCTR model is built upon a hybrid neural network framework with fine-grained modeling of latent representation and nonlinearity feature interactions for rating prediction. Specifically, convolution neural network is applied to extract effectively contextual features from textual information. Meanwhile, a fusion layer is exploited to combine features, and the multilayer perceptions are used to model the nonlinear interactions between the merged item latent features and user latent features.

Author(s) Name:  Donghua Liu, Jing Li, Bo Du, Jun Chang, Rong Gao & Yujia Wu

Journal name:   Knowledge and Information Systems

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s10115-020-01528-2

Volume Information:  volume 63, pages 621–646 (2021