Abstract
Recommender systems make use of different sources of information for providing users with recommendations of items. Such systems are often based on either collaborative filtering methods which make automatic predictions about the interests of a user, using preferences of similar users, or content based filtering that matches the user’s personal preferences with item characteristics. We adopt the content-based approach and propose to use the concept of resolving set that allows to determine the preferences of the users with a very limited number of ratings. We also show how to make recommendations when user ratings are imprecise or inconsistent, and we indicate how to take into account situations where users possibly don’t care about the attribute values of some items. All recommendations are obtained in a few seconds by solving integer programs.
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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. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Akhil, P., Shelbi, J.: A survey of recommender system types and its classification. Int. J. Adv. Res. Comput. Sci. 8(9), 486–491 (2017)
Beerliova, Z., Eberhard, F., Erlebach, T., Hall, A., Hoffmann, M., Mihalak, M., Ram, L.: Network discovery and verification. IEEE J. Sel. Areas Commun. 24(12), 2168–2181 (2006)
Berkovsky, S., Kuflik, T., Ricci, F.: Mediation of user models for enhanced personalization in recommender systems. User Model. User-Adap. Inter. 18(3), 245–286 (2008)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)
Chartrand, G., Eroha, L., Johnson, M., Oellermann, O.: Resolvability in graphs and the metric dimension of a graph. Discrete Appl. Math. 105, 99–113 (2000)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–71 (1992)
Harary, F., Melter, R.: On the metric dimension of a graph. Ars Comb. 2, 191–195 (1976)
Hertz, A.: An IP-based swapping algorithm for the metric dimension and minimal doubly resolving set problems in hypercubes. Optim Lett. 8, 1–13 (2017). https://doi.org/10.1007/s11590-017-1184-z
Khuller, S., Raghavachari, B., Rosenfeld, A.: Landmarks in graphs. Discrete Appl. Math. 70(3), 217–229 (1996)
Movielens website. https://movielens.org
Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Recommender Systems Handbook, pp. 1–35. Springer, Berlin (2011)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–59 (1997)
Slater, P.: Leaves of trees. Congr. Numer. 14, 549–559 (1975)
Webb, G.I.: Naïve Bayes. Encyclopedia of Machine Learning, pp. 713–714. Springer, Boston, MA (2011)
Yelp website. https://www.yelp.com
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Hertz, A., Kuflik, T. & Tuval, N. Resolving sets and integer programs for recommender systems. J Glob Optim 81, 153–178 (2021). https://doi.org/10.1007/s10898-020-00982-0
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DOI: https://doi.org/10.1007/s10898-020-00982-0