Abstract
Due to the advancement of technology and an increased number of digital devices per person, more and more digital data are generated daily. Extracting required data from such big data is a challenging task. Recommender systems help us in finding data that best match one’s taste. Collaborative filtering (CF) is the most popular approach used in recommender systems. Various similarity measure techniques are used in CF to calculate item-to-item and user-to-user similarity. The majority of these methods use common ratings to compute similarity. One of the similarity measurement methods is Jaccard similarity, which ignores both absolute values of ratings and the average rating value of a user. In this paper, we propose an improved measure that considers the ratio between absolute rating values and number of commonly rated items. We further improved the performance of proposed similarity measure by putting some thresholds on the average rating value of a user. An important aspect of ratings provided by a user is the rating preference behavior of a user, which almost all similarity measurement methods ignore. We also incorporated this behavior in our proposed method. The proposed method is tested over five publicly available datasets: Epinions, FilmTrust, Movie Lens-100K, CiaoDVD and MovieTweetings. The proposed method is compared with various modern similarity measures, and results show improvements in terms of prediction quality and accuracy.
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Shumeet, B.; Rohan, S.; Sivakumar, D.; Jing, Y.; Yagnik, J.; Kumar, S.; Ravichandran, D.; Aly, M.: Video suggestion and discovery for YouTube: taking random walks through the view graph. In: International Conference on World Wide Web, pp. 895–904 (2008)
Brynjolfsson, E.; Hu, Y.; Smith, M.D.: Consumer surplus in the digital economy: estimating the value of increased product variety at online booksellers. Manag. Sci. 49(11), 1580–1596 (2003)
Zheng, N.; Li, Q.; Shengcai, L.; Leiming, Z.: Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr. Inf. Sci. 36(6), 732–750 (2010)
Zhang, X.; Li, Y.: Use of collaborative recommendations for web search: an exploratory user study. J. Inf. Sci. 34(2), 145–161 (2008)
Miller, B.N.; Albert, I.; Lam, S.K.; Konstan, J.A.; Riedl, J.: MovieLens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 263–266
Billsus, D.; Brunk, C.A.; Evans, C.; Gladish, B.; Pazzani, M.: Adaptive interfaces for ubiquitous web access. Commun. ACM 45(5), 34–38 (2002)
Linden, G.; Smith, B.; York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Resnick, P.; Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Herlocker, J.L.; Konstan, J.A.; Terveen, L.G.; Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004)
Koutrica, G.; Bercovitz, B., Garcia, H.: FlexRecs: expressing and combining flexible recommendations. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 745–758 (2009)
Ayub, M.; Ghazanfar, M.; Maqssod, M.; Saleem, A.: A Jaccard base similarity measure to improve performance of CF based recommender systems. In: 32nd International Conference on Information Networking (ICOIN), Chiang Mai, Thailand, pp. 1–6 (2018)
www.netflixprize.com. Accessed 29 Aug 2017
Tuzhilin, A.; Adomavicius, G.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)
Polatidis, N.; Georgiadis, C.K.: A multi-level collaborative filtering method that improves recommendations. Expert Syst. Appl. 48, 100–110 (2016)
Shardanand, U.; Maes, P.: Social information filtering: algorithms for automating word of mouth. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217.
Guo, G.; Zhang, J.; Yorke-Smith, N.: A Novel Bayesian similarity measure for recommender systems. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pp. 2619–2625 (2013)
Liu, H.; Hu, Z.; Mian, A.; Tian, H.; Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl. Based Syst. 56, 156–166 (2014)
Jamali, M.; Ester, M.: 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. ACM (2009)
Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci. 178(1), 37–51 (2008)
Bobadilla, J.; Ortega, F.; Hernando, A.; Bernal, J.: A collaborative filtering similarity measure based on singularities. Inf. Process. Manag. 48, 204–217 (2012)
Cacheda, F.C.; Fernández, V.; Formoso, V.: Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web (TWEB) 5(1), 1–33 (2011)
Lu, J.; Shambour, Q.; Xu, Y.; Lin, Q.; Zhang, G.: A web-based personalized business partner recommendation system using fuzzy semantic techniques. Comput. Intell. 29(1), 37–69 (2013)
Wang, W.; Zhang, G.; Lu, J.: Collaborative filtering with entropy-driven user similarity in recommender systems. Int. J. Intell. Syst. 30(8), 854–870 (2015)
Bobadilla, J.; Ortega, F.; Hernando, A.; Bernal, J.: A collaborative filtering approach to mitigate the new user cold start problem. Knowl. Based Syst. 26, 225–238 (2011)
Bobadilla, J.; Hernando, A.; Orteqa, F.; Gutirrez, A.: Collaborative filtering based on significances. Inf. Sci. 185, 1–17 (2012)
Sun, S.-B.; Zhang, Z.-H.; Dong, X.-L.; Zhang, H.-R.; Li, T.-J.; Zhang, L.; Min, F.: Integrating triangle and Jaccard similarities for recommendation. PLoS ONE (2017). https://doi.org/10.1371/journal.pone.0183570
Patra, B.K.; Launonen, R.; Ollikainen, V.; Nandi, S.: A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl. Based Syst. 82, 163–177 (2015)
Sadasivam, S.; Saranya, K.G.: Modified heuristic similarity measure for personalization using collaborative filtering technique. Appl. Math. Inf. Sci. 11(01), 307–315 (2017)
Feng, J.; et al.: An improved collaborative filtering method based on similarity. PLoS ONE 13(9), e0204003 (2018)
Guo, G.; Zhang, J.; Thalmann, D.; Yorke-Smith, N.: ETAF: an extended trust antecedents framework for trust prediction. In: Proceedings of the 2014 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 540–547 (2014)
https://github.com/sidooms/MovieTweetings/tree/master/latest
Silveira, T., Zhang, M., Lin, X., Liu, Y., Ma, S.: How good your recommender system is? A survey on evaluations in recommendation. Int. J. Mach. Learn. Cybern. 10(5), 813–831 (2019)
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Ayub, M., Ghazanfar, M.A., Khan, T. et al. An Effective Model for Jaccard Coefficient to Increase the Performance of Collaborative Filtering. Arab J Sci Eng 45, 9997–10017 (2020). https://doi.org/10.1007/s13369-020-04568-6
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DOI: https://doi.org/10.1007/s13369-020-04568-6