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http://dx.doi.org/10.9708/jksci.2022.27.01.083

Integration of Similarity Values Reflecting Rating Time for 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.27, no.1, 2022 , pp. 83-89 More about this Journal
Abstract
As a representative technique of recommender systems, collaborative filtering has been successfully in service through many commercial and academic systems. This technique recommends items highly rated by similar neighbor users, based on similarity of ratings on common items rated by two users. Recently research on time-aware recommender systems has been conducted, which attempts to improve system performance by reflecting user rating time of items. However, the decay rate uniform to past ratings has a risk of lowering the rating prediction performance of the system. This study proposes a rating time-aware similarity measure between users, which is a novel approach different from previous ones. The proposed approach considers changes of similarity value over time, not item rating time. In order to evaluate performance of the proposed method, experiments using various parameter values and types of time change functions are conducted, resulting in improving prediction accuracy of existing traditional similarity measures significantly.
Keywords
Similarity Measure; Collaborative Filtering; Recommender System; Time-aware Recommender System;
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1 Y. Ding and X. Li, "Time Weight Collaborative Filtering," Fourteenth ACM international Conference on Information and Knowledge Management, pp. 485-492, 2005. DOI: 10.1145/1099554.1099689   DOI
2 Y. Huai-Zhen and L. Lei, "An Enhanced Collaborative Filtering Algorithm Based on Time Weight," International Symposium on Information Engineering and Electronic Commerce, pp. 262-265, 2009. DOI: 10.1109/IEEC.2009.61   DOI
3 L. He and F. Wu, "A Time-Context-Based Collaborative Filtering Algorithm," IEEE International Conference on Granular Computing, pp. 209-213, 2009. DOI: 10.1109/GRC.2009.5255130   DOI
4 A. Bahrehmand and R. Rafeh, "Proposing a New Metric for Collaborative Filtering," Journal of Software Engineering and Applications, Vol. 4, No. 7, pp. 411-416, 2011. DOI: 10.4236/jsea.2011.47047   DOI
5 N. Zheng and Q. Li, "A Recommender System based on Tag and Time Information for Social Tagging Systems," Expert Systems with Applications, Vol. 38, No. 4, pp. 4575-4587, 2011. DOI: 10.1016/j.eswa.2010.09.131   DOI
6 R. Rafeh and A. Bahrehmand, "An Adaptive Approach to Dealing with Unstable Behaviour of Users in Collaborative Filtering Systems," Journal of Information Science, Vol. 38, No. 3, pp. 205-221, 2012. DOI: 10.1177/0165551512437517   DOI
7 W. Lai and H. Deng, "An Improved Collaborative Filtering Algorithm Adapting to User Interest Changes," The 6th International Conference on New Trends in Information Science, Service Science and Data Mining, pp. 598-602, 2012.
8 P. G. Campos and F. Diez and I. Cantador, "Time-aware Recommender Systems: A Comprehensive Survey and Analysis of Existing Evaluation Protocols," User Modeling and User-Adapted Interaction, Vol. 24, No. 1, pp. 67-119, 2014. DOI: 10.1007/s11257-012-9136-x   DOI
9 G. Xu, Z. Tang, C. Ma, Y. Liu, and M. Daneshmand, "A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context," Journal of Electrical and Computer Engineering, Vol. 2019, Article ID 7070487, 12 pages, DOI: 10.1155/2019/7070487   DOI
10 C. Wangwatcharakul and S. Wongthanavasu, "A Novel Temporal Recommender System based on Multiple Transitions in User Preference Drift and Topic Review Evolution," Expert Systems with Applications, Vol. 185, 2021, Article Number 115626, DOI: 10.1016/j.eswa.2021.115626   DOI
11 N. Mohammadi and A. Rasoolzadegan, "A Two-stage Location-sensitive and User Preference-aware Recommendation System," Expert Systems with Applications, Vol. 191, 2022, Article Number 116188, DOI: 10.1016/j.eswa.2021.116188   DOI
12 J. Bobadilla, A. Hernando, F. Ortega, and J. Bernal, "A Framework for Collaborative Filtering Recommender Systems," Expert Systems with Applications, Vol. 38, No. 12, pp. 14609-14623, 2011. DOI: 10.1016/j.eswa.2011.05.021   DOI
13 A. Livne, E. S. Tov, A. Solomon, A. Elyasaf, B. Shapira, and L. Rokach, "Evolving Context-aware Recommender Systems with Users in Mind," Expert Systems with Applications, Vol. 189, 2022, Article Number 116042, DOI: 10.1016/j.eswa.2021.116042   DOI
14 K. Choi, D. Yoo, G. Kim, and Y. Suh, "A Hybrid Online-product Recommendation System: Combining Implicit Rating-based Collaborative Filtering and Sequential Pattern Analysis," Electronic Commerce Research and Applications, Vol. 11, No. 4, pp. 309-317, 2012. DOI: 10.1016/j.elerap.2012.02.004.   DOI
15 J. Bobadilla, F. Ortega, A. Hernando, and A. Gutierrez, " Recommender Systems Survey," Knowledge-based Systems, Vol. 46, pp. 109-132, 2013. DOI: 10.1016/j.knosys.2013.03.012   DOI
16 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
17 C. Desrosiers and G. Karypis, "A Comprehensive Survey of Neighborhood-based Recommendation Methods," Recommender Systems Handbook, pp. 107-144, Springer, Berlin, Germany, 2011. DOI: 10.1007/978-1-4899-7637-6_2   DOI
18 H. Khojamli and J. Razmara, "Survey of Similarity Functions on Neighborhood-based Collaborative Filtering," Expert Systems with Applications, Vol. 185, 2021, Article Number 115482, DOI: 10.1016/j.eswa.2021.115482   DOI