Abstract
Collaborative filtering has been successfully implemented in many commercial recommender systems. These systems recommend items favored by other users with similar preference history to the current user. As finding similar users is critical to the performance of the system, various techniques have been suggested to develop similarity measures. However, there are still much to be improved, because existing similarity measures simply utilize additional heuristic information and seldom reflect the global rating behaviors on items. This paper aims to improve the previous similarity measures by employing the information entropy of user ratings so that the user’s global rating behavior on items can be reflected. The efficiency of the proposed method is examined through extensive experiments to demonstrate its superior performance over the previous similarity measures especially in small-scaled and sparse datasets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aamir M, Bhusry M (2015) Recommendation system: state of the art approach. Int J Comput Appl 120(12):25–32. https://doi.org/10.5120/21281-4200
Ahn H (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci 178(1):37–51. https://doi.org/10.1016/j.ins.2007.07.024
Baltrunas L, Makcinskas T, Ricci F (2010) Group recommendation with rank aggregation and collaborative filtering. In: Proceedings of the 2010 ACM conference on recommender systems, pp 119–126. https://doi.org/10.1145/1864708.1864733
Bobadilla J, Serradilla F, Bernal J (2010) A new collaborative filtering metric that improves the behavior of recommender systems. Knowl Based Syst 23(6):520–528. https://doi.org/10.1016/j.knosys.2010.03.009
Bobadilla J, Hernando A, Ortega F, Gutierrez A (2012a) Collaborative filtering based on significances. Inf Sci 185(1):1–17. https://doi.org/10.1016/j.ins.2011.09.014
Bobadilla J, Ortega F, Hernando A (2012b) A collaborative filtering similarity measure based on singularities. Inf Process Manage 48(2):204–217. https://doi.org/10.1016/j.ipm.2011.03.007
Bobadilla J, Gutierrez A, Ortega F, Zhu B (2018) Reliability quality measures for recommender systems. Inf Sci 442–443:145–157. https://doi.org/10.1016/j.ins.2018.02.030
Cacheda F, Carneiro V, Fernandez D, Formoso V (2011) Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web 5(1):1–33. https://doi.org/10.1145/1921591.1921593
Chandrashekhar H, Bhasker B (2011) Personalized recommender system using entropy based collaborative filtering technique. J Electron Commer Res 12(3):214–237
Chen CC, Wan YH, Chung MC, Sun YC (2013) An effective recommendation method for cold start new users using trust and distrust networks. Inf Sci 224:19–36. https://doi.org/10.1016/j.ins.2012.10.037
Chen H, Li Z, Hu W (2016) An improved collaborative recommendation algorithm based on optimized user similarity. J Supercomput 72(7):2565–2578. https://doi.org/10.1007/s11227-015-1518-5
Desrosiers C, Karypis G (2010) A novel approach to compute similarities and its application to item recommendation. In: PRICAI 2010 trends in artificial intelligence, lecture notes in computer science 6230/2010:39–51. https://doi.org/10.1007/978-3-642-15246-7_7
Herlocker JL, Konstan JA, Riedl JT (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr 5(4):287–310. https://doi.org/10.1023/A%3A1020443909834
Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 397–406. https://doi.org/10.1145/1557019.1557067
Jiang L, Cheng Y, Yang L, Li J, Yan H, Wang X (2018) A trust-based collaborative filtering algorithm for E-commerce recommendation system. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0928-7
Kosmides P, Demestichas K, Adamopoulou E, Remoundou C, Loumiotis I, Theologou M, Anagnostou M (2016) Providing recommendations on location-based social networks. J Ambient Intell Humaniz Comput 7(4):567–578. https://doi.org/10.1007/s12652-016-0346-7
Koutrica G, Bercovitz B, Garcia H (2009) FlexRecs: expressing and combining flexible recommendations. In: SIGMOD conference, pp 745–758. https://doi.org/10.1145/1559845.1559923
Kullback S, Leibler R (1951) On information and sufficiency. Ann Math Stat 22(1):79–86. https://doi.org/10.1214/aoms/1177729694
Kwon HJ, Lee TH, Hong KS (2009) Improving prediction accuracy using entropy weighting in collaborative filtering. In: Symposia and workshops on ubiquitous, autonomic and trusted computing, pp 40–45. https://doi.org/10.1109/UIC-ATC.2009.50
Lee S (2017) Improving Jaccard index for measuring similarity in collaborative filtering. Lect Notes Electr Eng 424:799–806. https://doi.org/10.1007/978-981-10-4154-9_93
Li M, Zheng K (2015) A collaborative filtering algorithm combined with user habits for rating. In: International conference on logistics engineering, management and computer science, pp 1279–1282. https://doi.org/10.2991/lemcs-15.2015.255
Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl Based Syst 56:156–166. https://doi.org/10.1016/j.knosys.2013.11.006
Noia TD, Rosati J, Tomeo P, Sciascio ED (2017) Adaptive multi-attribute diversity for recommender system. Inf Sci 382–383:234–253. https://doi.org/10.1016/j.ins.2016.11.015
Piao CH, Zhao J, Zheng LJ (2009) Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce. SOCA 3(2):147–157. https://doi.org/10.1007/s11761-008-0034-3
Ren L, Gu J, Xia W (2011) A weighted similarity-boosted collaborative filtering approach. Energy Procedia 13:9060–9067. https://doi.org/10.1016/j.egypro.2011.12.696
Resnick P, Lakovou N, Sushak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of Netnews. In: Proc. the ACM conference on computer supported cooperative work, ACM Press, pp 175–186. https://doi.org/10.1145/192844.192905
Saranya KG, Sadasivam GS, Chandralekha M (2016) Performance comparison of different similarity measures for collaborative filtering technique. Indian J Sci Technol 9(29):1–8. https://doi.org/10.17485/ijst/2016/v9i29/91060
Shannon CE (1951) Prediction and entropy of printed English. Bell Syst Tech J 30:50–64. https://doi.org/10.1002/j.1538-7305.1951.tb01366.x
Shi Y, Larson M, Hanjalic A (2013) Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation. Inf Sci 229(20):29–39. https://doi.org/10.1016/j.ins.2012.12.002
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell. https://doi.org/10.1155/2009/421425
Wang W, Zhang G, Lu J (2015) Collaborative filtering with entropy-driven user similarity in recommender systems. Int J Intell Syst 30(8):854–870. https://doi.org/10.1002/int.21735
Wang Y, Deng J, Gao J, Zhang P (2017) A hybrid user similarity model for collaborative filtering. Inf Sci 418–419:102–118. https://doi.org/10.1016/j.ins.2017.08.008
Xia P, Zhang L, Li F (2015) Learning similarity with cosine similarity ensemble. Inf Sci 307:39–52. https://doi.org/10.1016/j.ins.2015.02.024
Xiao J, Wang M, Jiang B, Li J (2018) A personalized recommendation system with combinational algorithm for online learning. J Ambient Intell Humaniz Comput 9(3):667–677. https://doi.org/10.1007/s12652-017-0466-8
Yang Y, Hooshyar D, Jo J, Lim HS (2018) A group preference-based item similarity model: comparison of clustering techniques in ambient and context-aware recommender systems. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-1039-1
Zhu B, Hurtado R, Bobadilla J, Ortega F (2018) An efficient recommender system method based on the numerical relevances and the non-numerical structures of the ratings. IEEE Access 6:49935–49954. https://doi.org/10.1109/ACCESS.2018.2868464
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lee, S. Using entropy for similarity measures in collaborative filtering. J Ambient Intell Human Comput 11, 363–374 (2020). https://doi.org/10.1007/s12652-019-01226-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01226-0