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Recent advances in information filtering have resulted in effective recommender systems that are able to provide online personalized recommendations to millions of users from all over the world. However, most of these systems ignore the explanation purpose while producing recommendations with high-quality results. Moreover, the classification of reviews given to users as explanations is not fully exploited in previous studies. In this paper, we develop a convolutional neural network-based reviews classification method for explainable recommendation systems. The convolutional neural network is used to extract the reviews features for predicting whether the reviews provided as explanations are positive or negative. Based on such additional information, users can understand not only why certain items are recommended for them but also get support to know the nature of such explanations. We conduct experiments on a dataset from Amazon. The experimental results show that our method outperforms state-of-the-art methods.

Item Type	Conference
Divisions	» Laboratory of Computer Science and Mathematics » Faculty of Science and Technology
ePrint ID	2544
Date Deposited	2021-02-05
Further Information	Google Scholar
URI	https://univ-soukahras.dz/en/publication/article/2544

@inproceedings{uniusa2544,
    title={A convolutional neural network-based reviews classification method for explainable recommendations},
    author={Hafed ZARZOUR, Bashar Al shboul, Mahmoud Al-Ayyoub and Yaser Jararweh},
    year={2020},
    booktitle={IEEE 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS)}
}