Abstract
In recommender systems (RSs), getting high accuracy alone does not improve the user satisfaction. In the literature, efforts are being made to improve the variety/diversity of recommendations for higher user satisfaction. In these efforts, the accuracy performance is reduced at the cost of improving the variety of recommendations. In this paper, we propose an approach to improve the diversity as well as accuracy of association rule based RSs. We propose a ranking mechanism, called diverse rank, to rank association rules based on the diversity of the items in the pattern. The recommendations are made based on association rules with high confidence and diversity. The experimental results on the real-world MovieLens data set show that the proposed approach improves the performance of association rule based RSs with better diversity without compromising the accuracy.
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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. TKDE 17(6), 734–749 (2005)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD, pp. 207–216. ACM (1993)
Basu Roy, S., Amer-Yahia, S., Chawla, A., Das, G., Yu, C.: Constructing and exploring composite items. In: Proceedings of the SIGMOD, pp. 843–854. ACM (2010)
Bradley, K., Smyth, B.: Improving recommendation diversity. In: Proceedings of the 12th AICS, pp. 75–84 (2001)
Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. In: Proceedings of the 21th VLDB, pp. 420–431. Morgan Kaufmann Publishers Inc. (1995)
IMDb: Internet movie database. http://www.imdb.com/genre/ (2014), [Online; accessed October-2014]
Kumara Swamy, M., Reddy, P.K., Srivastava, S.: Extracting diverse patterns with unbalanced concept hierarchy. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part I. LNCS, vol. 8443, pp. 15–27. Springer, Heidelberg (2014)
Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. Data Min. Knowl. Discov. 6(1), 83–105 (2002)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI 2006, pp. 1097–1101. ACM (2006)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: 2nd Conference on e-Commerce, pp. 158–167. ACM (2000)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4:2–4:2 (2009)
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th WWW, pp. 22–32. ACM (2005)
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Kumara Swamy, M., Krishna Reddy, P. (2015). Improving Diversity Performance of Association Rule Based Recommender Systems. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_34
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DOI: https://doi.org/10.1007/978-3-319-22849-5_34
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