Abstract Recommendation systems are information filtering software that delivers suggestions about relevant stuff from a massive collection of data. Collaborative filtering approaches are the most popular in recommendations. The primary concern of any recommender system is to provide favorable recommendations based on the rating prediction of user preferences. In this article, we propose a novel discretization based framework for collaborative filtering to improve rating prediction. Our framework includes discretization-based preprocessing, chi-square based attribution selection, and K-Nearest Neighbors (KNN) based similarity computation. Rating prediction affords some basis for the judgment to decide whether recommendations are generated or not, subject to the ratio of performance of any recommendation system. Experiments on two datasets MovieLens and BookCrossing, demonstrate the effectiveness of our method.

References

[1] Adomavicius G. and Tuzhilin A., “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734- 749, 2005.

[2] Ahmed B., Wang L., Hussain W., Qadoos M., Tingyi Z., Amjad M., Badar-ud-Duja S., Hussain A., and Raheel M., “Optimal Rating Prediction in Recommender Systems,” in Proceedings of International Conference on Data Service, Ningbo, pp. 331-339, 2019.

[3] Bellogín A., “Performance Prediction in Recommender Systems,” in Proceedings of International Conference on User Modeling, Adaptation, and Personalization, Girona, pp. 401-404, 2011.

[4] Bellogín A., Castells P., and Cantador I., “Predicting the Performance of Recommender Systems: An Information Theoretic Approach,” in Proceedings of Conference on the Theory of Information Retrieval, Bertinoro, pp. 27-39, 2011.

[5] Bobadilla J., Ortega F., Hernando A., and Gutiérrez A., “Recommender Systems Survey,” Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.

[6] Cremonesi P., Koren Y., and Turrin R., “Performance of Recommender Algorithms on Top-N Recommendation Tasks,” in Proceedings of the 4th ACM Conference on Recommender Systems, New York, pp. 39-46, 2010.

[7] Das A., Datar M., Garg A., and Rajaram S., “Google News Personalization: Scalable Online Collaborative Filtering,” in Proceedings of the 16th international Conference on World Wide Web, New York, pp. 271-280, 2007.

[8] Ekstrand M., Riedl J., and Konstan J., Collaborative Filtering Recommender Systems, Now the Essence of Knowledge, 2011.

[9] Fletcher K. and Liu X., “A Collaborative Filtering Method for Personalized Preference- Based Service Recommendation,” in Proceedings of IEEE International Conference on Web Services, New York, pp. 400-407, 2015.

[10] Gao S., Yu Z., Shi L., Yan X., and Song H., “Review Expert Collaborative Recommendation Algorithm Based on Topic Relationship,” Journal of Automatica Sinica, vol. 2, no. 4, pp. 403-411, 2015.

[11] Gunawardana A. and Shani G., “A Survey of Accuracy Evaluation Metrics of Recommendation Tasks,” Journal of Machine Learning Research, vol. 10, pp. 2935-2962, 2009.

[12] He X., Min F., and Zhu W., “A Comparative Study of Discretization Approaches for Granular Association Rule Mining,” in Proceedings of 26th IEEE Canadian Conference on Electrical and Computer Engineering, Regina, pp. 1-5, 2013.

[13] Hofmann T., “Latent Semantic Models for Collaborative Filtering,” ACM Transactions on Information Systems, vol. 22, no. 1, pp. 89-115, 2004.

[14] Koren Y., Bell R., and Volinsky C., “Matrix Factorization Techniques for Recommender Systems,” Computer Society, vol. 42, no. 8, pp. 30-37, 2009.

[15] Lee J., Sun M., and Lebanon G., “A Comparative Study of Collaborative Filtering Algorithms,” arXiv preprint arXiv:1205.3193, pp. 1-27, 2012.

[16] Li Z., Huang M., and Zhang., “A Collaborative Filtering Algorithm of Calculating Similarity Based on Item Rating and Attributes,” in Proceedings of 14th Web Information Systems and Applications Conference, Liuzhou, pp. 215- 218, 2017.

[17] Liu H. and Setiono R., “Chi2: Feature Selection and Discretization of Numeric Attributes,” in Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, Herndon, pp. 388-391, 1995.

[18] Lu J., Wu D., Mao M., Wang W., and Zhang G., “Recommender System Application Developments: A Survey,” Decision Support Systems, vol. 74, pp. 12-32, 2015.

[19] Luo X., Zhou M., Xia Y., and Zhu Q., “An Efficient Non-Negative Matrix-Factorization- Based Approach to Collaborative Filtering for Recommender Systems,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1273- 1284, 2014.

[20] Ma H., Yang H., Lyu M. R., and King I., “Sorec: Social Recommendation Using Probabilistic Matrix Factorization,” in Proceedings of the 17th Discretization Based Framework to Improve the Recommendation Quality 371 ACM Conference on Information and Knowledge Management, New York, pp. 931-940, 2008.

[21] Mohamed M., Khafagy M., and Ibrahim M., “Recommender Systems Challenges and Solutions Survey,” in Proceedings of International Conference on Innovative Trends in Computer Engineering, Aswan, pp. 149-155, 2019.

[22] Pazzani M. and Billsus D., the Adaptive Web, Springer Link, 2007.

[23] Periyasamy K., Jaiganesh J., Ponnambalam K., Rajasekar J., and Arputharaj K., “Analysis and Performance Evaluation of Cosine Neighbourhood Recommender System,” The International Arab Journal of Information Technology, vol. 14, no. 5, pp. 747-754, 2017.

[24] Resnick P., Iacovou N., Suchak M., Bergstrom P., and Riedl J., “Grouplens: An Open Architecture for Collaborative Filtering of Netnews,” in Proceedings of the ACM Conference on Computer Supported Cooperative Work, New York, pp. 175-186, 1994.

[25] Rennie J. and Srebro N., “Fast Maximum Margin Matrix Factorization for Collaborative Prediction,” in Proceedings of the 22nd International Conference on Machine Learning, Germany, pp. 713-719, 2005.

[26] Rennie J. and Srebro N., “Fast Maximum Margin Matrix Factorization for Collaborative Prediction,” in Proceedings of the 22nd International Conference on Machine Learning, Germany, pp. 713-719, 2005.

[27] Sarwar B., Karypis G., Konstan J., and Riedl J., “Item-Based Collaborative Filtering Recommendation Algorithms,” in Proceedings of the 10th international conference on World Wide Web, New York, pp. 285-295, 2001.

[28] Sarwar B., Karypis G., Konstan J., and Riedl J., “Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems,” in Proceedings of 5th International Conference on Computer and Information Science, Aswan, pp. 8-27, 2002.

[29] Shan H. and Banerjee A., “Generalized Probabilistic Matrix Factorizations for Collaborative Filtering,” in Proceedings of IEEE International Conference on Data Mining, Sydney, pp. 1025-1030, 2010.

[30] Steck H., “Evaluation of Recommendations: Rating Prediction and Ranking,” in Proceedings of the 7th ACM Conference on Recommender Systems, New York, pp. 213-220, 2013. Bilal Ahmed is a PhD candidate in Taiyuan University of Technology, China. He received his MS degree from Pakistan. His research areas include machine learning, deep learning and recommendation systems. Wang Li is a professor and PhD supervisor in Data science college, Taiyuan University of Technology, China. Her research area is big data computing, machine learning and recommendation systems.