Abstract
We present ReInCre (Demo video available at https://youtu.be/MyFczz7Vefo) as a solution demo for incorporating user rating credibility in Collaborative Filtering (CF) approach to enhance the recommendation performance. The credibility values of users are calculated according to their rating behavior and they are utilized in discovering the neighbors (Code available at https://github.com/NaimeRanjbarKermany/Cred). To the best of our knowledge, it is the first work to incorporate the rating credibility of users in a CF recommendation. Our approach works as a powerful add-on to existing CF-based recommender systems in order to optimize the neighborhood. Experiments are conducted on the real-world dataset from Yahoo! Movies. Comparing with the baselines, the experimental results show that our proposed method significantly improves the quality of recommendation in terms of precision and \(F_1\)-measure. In particular, the standard deviation of the errors between the prediction values and the real ratings becomes much smaller by incorporating credibility measurements of the users.
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Notes
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refer to https://github.com/NaimeRanjbarKermany/Cred/ to see the results on other datasets.
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Kermany, N.R., Zhao, W., Yang, J., Wu, J. (2020). ReInCre: Enhancing Collaborative Filtering Recommendations by Incorporating User Rating Credibility. In: U, L., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds) Web Information Systems Engineering. WISE 2020. Communications in Computer and Information Science, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-3281-8_7
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