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

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Abstract

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. Experimental results over five real-world datasets show that NCTR significantly outperforms several state-of-the-art recommendation methods. Source codes are available in https://github.com/luojia527/NCTR_master.

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Notes

  1. http://grouplens.org/datasets/movielens/.

  2. http://jmcauley.ucsd.edu/data/amazon/.

  3. Plot summaries are available at http://www.imdb.com/.

  4. https://www.tensorflow.org/.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the editor for their thankful comments and suggestions. This work is supported in part by National Natural Science Foundation of China under Grants 61822113, 41871243, the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) under Grant 2019AEA170.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Donghua Liu, Jing Li, Bo Du, Jun Chang & Yujia Wu

  2. School of Computer Science, Hubei University of Technology, Wuhan, 430068, China

    Rong Gao

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  1. Donghua Liu
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Correspondence to Jing Li.

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Liu, D., Li, J., Du, B. et al. A hybrid neural network approach to combine textual information and rating information for item recommendation. Knowl Inf Syst 63, 621–646 (2021). https://doi.org/10.1007/s10115-020-01528-2

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