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http://dx.doi.org/10.9708/jksci.2019.24.12.093

Using Genre Rating Information for Similarity Estimation in Collaborative Filtering  

Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.12, 2019 , pp. 93-100 More about this Journal
Abstract
Similarity computation is very crucial to performance of memory-based collaborative filtering systems. These systems make use of user ratings to recommend products to customers in online commercial sites. For better recommendation, most similar users to the active user need to be selected for their references. There have been numerous similarity measures developed in literature, most of which suffer from data sparsity or cold start problems. This paper intends to extract preference information as much as possible from user ratings to compute more reliable similarity even in a sparse data condition, as compared to previous similarity measures. We propose a new similarity measure which relies not only on user ratings but also on movie genre information provided by the dataset. Performance experiments of the proposed measure and previous relevant measures are conducted to investigate their performance. As a result, it is found that the proposed measure yields better or comparable achievements in terms of major performance metrics.
Keywords
Collaborative Filtering; Recommender System; Similarity Measure; Data Sparsity Problem; Cold-start problem;
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1 X. Su and T.M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, 2009. DOI:10.1155/2009/421425
2 S. Du, H. Zhang, H. Xu, J. Yang, and O. Tu, "To Make the Travel Healthier: A New Tourism Personalized Route Recommendation Algorithm," Journal of Ambient Intelligence and Humanized Computing, Vol. 10, No. 9, pp. 3551-3562, 2019. DOI:10.1007/s12652-018-1081-z   DOI
3 J. Gupta and J. Gadge, "Performance Analysis of Recommendation System based on Collaborative Filtering and Demographics," International Conference on Communication Information & Computing Technology, pp. 1-6, 2015. DOI: 10.1109/ICCICT.2015.7045675
4 M. Aamir and M. Bhusry, “Recommendation System: State of the Art Approach,” International Journal Computer Applications, Vol. 120, No. 12, pp. 25-32, 2015. DOI: 10.5120/21281-4200   DOI
5 M. Jalili, S. Ahmadian, M. Izadi, P. Moradi, and M. Salehi, "Evaluating Collaborative Filtering Recommender Algorithms: A Survey," IEEE Access, Vol. 6, pp. 74003-74024, 2018. DOI: 10.1109/ACCESS.2018.2883742   DOI
6 K.G. Saranya, G.S. Sadasivam, and M. Chandralekha, "Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique," Indian Journal of Science and Technology, Vol. 9, No. 29, 2016. DOI: 10.17485/ijst/2016/v9i29/91060
7 Z. Y. Hafshejani, M. Kaedi, and A. Fatemi, “Improving Sparsity and New User Problems in Collaborative Filtering by Clustering the Personality Factors,” Electronic Commerce Research, Vol. 18, No. 4, pp. 813-836, 2018. DOI: 10.1007/s10660-018-9287-x   DOI
8 Koohi and K. Kiani, "A New Method to Find Neighbor Users that Improves the Performance of Collaborative Filtering," Expert Systems With Applications, Vol. 83, pp. 30-39, 2017. DOI: 10.1016/j.eswa.2017.04.027   DOI
9 M. Li and K. Zheng, "A Collaborative Filtering Algorithm Combined with User Habits for Rating," International Conference on Logistics Engineering, Management and Computer Science, pp 1279-1282, 2015. DOI: 10.2991/lemcs-15.2015.255
10 B. Zhu, R. Hurtado, J. Bobadilla, and F. Ortega, "An Efficient Recommender System Method based on the Numerical Relevances and the Non-numerical Structures of the Ratings," IEEE Access, Vol. 6, pp. 49935-49954, 2018. DOI: 10.1109/ACCESS.2018.2868464   DOI
11 W. Wang, G. Zhang, and J. Lu, “Collaborative Filtering with Entropy-driven User Similarity in Recommender Systems,” International Journal of Intelligent Systems, Vol. 30, No. 8, pp. 854-870, 2015. DOI: 10.1002/int.21735   DOI
12 S. Lee, "Using Entropy for Similarity Measures in Collaborative Filtering," Journal of Ambient Intelligence and Humanized Computing, Feb. 2019. DOI: 10.1007/s12652-019-01226-0
13 G. Koutrica, B. Bercovitz, and H. Garcia, "FlexRecs: Expressing and Combining Flexible Recommendations," Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, pp. 745-758, 2009.
14 J. Bobadilla, F. Serradilla, and J. Bernal, “A New Collaborative Filtering Metric that Improves the Behavior of Recommender Systems,” Knowledge Based Systems, Vol. 23, No. 6, pp. 520-528, 2010. DOI: 10.1016/j.knosys.2010.03.009   DOI
15 S. Lee, "Improving Jaccard Index for Measuring Similarity in Collaborative Filtering," Lecture Notes in Electrical Engineering, Vol. 424, pp. 799-806, 2017. DOI: 10.1007/978-981-10-4154-9_93   DOI
16 M. Salehi, I. N. Kamalabadi, and M. B. Ghaznavi-Ghoushchi, "Attribute-based Collaborative Filtering using Genetic Algorithm and Weighted C-means Algorithm," International Journal of Business Information Systems, Vol. 13, No. 3, pp. 265-283, 2013. DOI: 10.1504/IJBIS.2013.054465   DOI
17 F. Cacheda, V. Carneiro, D. Fernandez, and V. Formoso, “Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-performance Recommender Systems,” ACM Transactions on the Web, Vol. 5, No. 1, pp. 1-33, 2011. DOI: 10.1145/1921591.1921593