Abstract
Recommender systems (RSs) are utilized by various e-commerce giants such as Amazon, YouTube and Netflix for providing a personalized experience to the individual users. For this reason, it has become very important to develop and use efficient techniques to provide recommendations. Neighborhood-based collaborative filtering approaches are traditional techniques for recommendations and are very popular due to their simplicity and efficiency. Neighborhood-based recommender systems use numerous kinds of similarity measures for finding similar users or items. Further, defining novel ways to model the notion of similarity is an active thread of research among RS researchers (by using rating data available in the user-item matrix). This work compares and analyzes the performance of various Jaccard-based similarity measures. Primarily, various Jaccard-based similarity measures are examined with their authoritative definitions. We have conducted various experiments using standardized benchmark datasets (MovieLens-100 K, MovieLens-1 M, and Yahoo music) for assessing the performance of these measures. Empirically obtained results demonstrate that the New Weighted Similarity Measure (NWSM) provides better predictive accuracy among all measures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ricci F, Rokach L, Shapira B, Kantor PB (2010) Recommender systems handbook, 1st edn. Springer-Verlag, Berlin, Heidelberg
Aggarwal CC (2016) Recommender systems: the textbook, 1st edn. Springer Publishing Company, Incorporated
Ekstrand MD (2011) Collaborative filtering recommender systems. Found. Trends® Human-Comp Int 4(2):81–173
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell Section 3:1–19
Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proc 14th Conf Uncertain Artif Intell 461(8):43–52
Joaquin D, Naohiro I (1999) Memory-based weighted-majority prediction for recommender systems. Res Dev Inf Retr
Nakamura A, Abe N (1998) Collaborative filtering using weighted majority prediction algorithms. In Proceedings of the Fifteenth International Conference on Machine Learning, pp 395–403
Getoor L, Sahami M (1999) Using probabilistic relational models for collaborative filtering. Work Web Usage Anal User Profiling
Marlin B (2003) Modeling user rating profiles for collaborative filtering. In Proceedings of the 16th International Conference on Neural Information Processing Systems, pp 627–634
Herlocker JON, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr Boston 287–310
Bobadilla J, Serradilla F, Bernal J (2010) A new collaborative filtering metric that improves the behavior of recommender systems. Knowledge-Based Syst 23(6):520–528
Bobadilla J, Ortega F, Hernando A, Arroyo Á (2012) A balanced memory-based collaborative filtering similarity measure. Int J Intell Syst 27(10):939–946
Bag S, Kumar SK, Tiwari MK (2019) An efficient recommendation generation using relevant Jaccard similarity. Inf Sci (Ny) 483:53–64
Wu X, Huang Y, Wang S (2017) A new similarity computation method in collaborative filtering based recommendation system. In 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), pp 1–5
Lee S (2017) Improving Jaccard index for measuring similarity in collaborative filtering. Inf Sci Appl 2017:799–806
Suryakant, Mahara T (2016) A new similarity measure based on mean measure of divergence for collaborative filtering in sparse environment. Proc Comput Sci 89:450–456
Zang X et al (2017) A new weighted similarity method based on neighborhood user contributions for collaborative filtering. Proc—2016 IEEE 1st Int Conf. Data Sci Cyberspace, DSC 2016, pp 376–381
Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Syst 56:156–166
Suganeshwari G, Syed Ibrahim SP (2018) A comparison study on similarity measures in collaborative filtering algorithms for movie recommendation. Int J Pure Appl Math 119(15 Special Issue C):1495–1505
Sondur SD, Nayak S, Chigadani AP (2016) Similarity measures for recommender systems: a comparative study. Int J Sci Res Dev 2(3):76–80
Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Group Lens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp 175–186
Jaccard P (1901) Distribution comparée de la flore alpine dans quelques régions des Alpes occidentales et orientales. Bull la Socit Vaudoise des Sci Nat 37:241–272
Verma V, Aggarwal RK (2019) Accuracy assessment of similarity measures in collaborative recommendations using CF4J framework. Int J Mod Educ Comput Sci 11(5):41
Ortega F, Zhu B, Bobadilla J, Hernando A (2018) CF4J: collaborative filtering for Java. Knowledge-Based Syst 152:94–99
Arsan T, Koksal E, Bozkus Z (2016) Comparison of collaborative filtering algorithms with various similarity measures for movie recommendation. Int J Comput Sci Eng Appl 6(3):1–20
Owen S, Anil R, Dunning T, Friedman E (2011) Mahout in action. Manning Publications Co, Greenwich, CT, USA
Al Hassanieh L, Jaoudeh CA, Abdo JB, Demerjian J (2018) Similarity measures for collaborative filtering recommender systems. In 2018 IEEE Middle East North Africa Commun. Conf. MENACOMM, pp 1–5
Stephen SC, **e H, Rai S (2017) Measures of similarity in memory-based collaborative filtering recommender system—a comparison. ACM Int Conf Proc Ser Part F1296
Sun SB et al (2017) Integrating triangle and jaccard similarities for recommendation. PLoS ONE 12(8):1–16
Verma V, Aggarwal RK (2019) A new similarity measure based on simple matching coefficient for improving the accuracy of collaborative recommendations. Int J Inf Technol Comput Sci (IJITCS) 6:37–49
Al-bashiri H, Abdulgabber MA, Romli A, Hujainah F (2017) Collaborative filtering similarity measures: revisiting. ACM Int Conf Proc Ser Part F1312:195–200
Verma V, Aggarwal RK (2020) A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: empirical and theoretical perspective. Soc Netw Anal Min 10(1)
Shardanand U, Maes P (1995) Social information filtering: algorithms for automating ‘Word of Mouth’. Proc SIGCHI Conf Hum factors Comput Syst - CHI ’95, pp 210–217
Ahn HJ (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci (Ny) 178(1):37–51
MovieLens | GroupLens. [Online]. https://grouplens.org/datasets/movielens/. Accessed 22 Dec 2018
Webscope | Yahoo Labs. [Online]. https://webscope.sandbox.yahoo.com/. Accessed 16 May 2020
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
**dal, A., Sharma, N., Verma, V. (2022). Joyful Jaccard: An Analysis of Jaccard-Based Similarity Measures in Collaborative Recommendations. In: Sanyal, G., Travieso-González, C.M., Awasthi, S., Pinto, C.M.A., Purushothama, B.R. (eds) International Conference on Artificial Intelligence and Sustainable Engineering. Lecture Notes in Electrical Engineering, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-16-8542-2_3
Download citation
DOI: https://doi.org/10.1007/978-981-16-8542-2_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8541-5
Online ISBN: 978-981-16-8542-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)