Abstract
The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to individuals to help them in their exploration tasks. In the ideal case, these recommendations, except of being accurate, should be also novel. However, up to now most platforms fail to provide both novel and accurate recommendations. For example, a well-known recommendation algorithm, such as matrix factorization (MF), tries to optimize only the accuracy criterion, while disregards the novelty of recommended items. In this paper, we propose a new model, denoted as popularity-based NMF, that allows to trade-off the MF performance with respect to the criteria of novelty, while only minimally compromising on accuracy. Our experimental results demonstrate that we attain high accuracy by recommending also novel items.
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Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44503-X_27
Castells, P., Hurley, N.J., Vargas, S.: Novelty and diversity in recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 881–918. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_26
Charles, C., et al.: Novelty and diversity in information retrieval evaluation. In: SIGIR Conference, SIGIR 2008, pp. 659–666 (2008)
Cheng, P., Wang, S., Ma, J., Sun, J., Xiong, H.: Learning to recommend accurate and diverse items. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 183–192 (2017). https://doi.org/10.1145/3038912.3052585
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 39–46. ACM, New York (2010). https://doi.org/10.1145/1864708.1864721
De Gemmis, M., Lops, P., Semeraro, G., Musto, C.: An investigation on the serendipity problem in recommender systems. Inf. Process. Manag. 51(5), 695–717 (2015). https://doi.org/10.1016/j.ipm.2015.06.008. https://www.sciencedirect.com/science/article/pii/S0306457315000837
Dhillon, I.S., Sra, S.: Generalized nonnegative matrix approximations with Bregman divergences. In: NIPS, pp. 283–290 (2005)
Furnas, G., Deerwester, S., Dumais, S.T.: Information retrieval using a singular value decomposition model of latent semantic structure. In: Proccedings ACM SIGIR Conference, pp. 465–480 (1988)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1–19:19 (2015). https://doi.org/10.1145/2827872
Harper, F.M., Konstan, J.A.: The MovieLens datasets. ACM Trans. Interact. Intell. Syst. 5(4), 1–19 (2015)
Hurley, N.J.: Personalised ranking with diversity. Proceedings of the 7th ACM Conference on Recommender Systems - RecSys 2013, vol. 2, no. 1, pp. 379–382 (2013). https://doi.org/10.1145/2507157.2507226. http://dl.acm.org/citation.cfm?doid=2507157.2507226
Jannach, D., Lerche, L., Kamehkhosh, I., Jugovac, M.: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Model. User-Adap. Inter. 25(5), 427–491 (2015)
Jannach, D., Resnick, P., Tuzhilin, A., Zanker, M.: Recommender systems - beyond matrix completion. Commun. ACM 59(11), 94–102 (2016). https://doi.org/10.1145/2891406
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 42–49 (2009). https://doi.org/10.1109/MC.2009.263
Lee, D.D., Seung, H.S.: Learning the parts of objects by nonnegative matrix factorization. Nature 401, 788–791 (1999)
Lin, C.J.: On the convergence of multiplicative update algorithms for nonnegative matrix factorization. IEEE Trans. Neural Netw. 18(6), 1589–1596 (2007)
Mahoney, M.W., Drineas, P.: Cur matrix decompositions for improved data analysis. Proc. Nat. Acad. Sci. 106(3), 697–702 (2009). https://doi.org/10.1073/pnas.0803205106. http://www.pnas.org/content/106/3/697.abstract
Ning, X., Karypis, G.: SLIM: Sparse linear methods for top-N recommender systems. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 497–506. IEEE (2011)
Ning, X., Karypis, G.: Sparse linear methods with side information for top-n recommendations. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 155–162. ACM (2012)
Rendle, S., Freudenthaler, C., Gantner, Z., Lars, S.T.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452–461. AUAI Press, Arlington (2009). http://portal.acm.org/citation.cfm?id=1795114.1795167
Tomeo, P., Di Noia, T., De Gemmis, M., Lops, P., Semeraro, G., Sciascio, E.D.: Exploiting regression trees as user models for intent-aware multi-attribute diversity. Technical report. http://grouplens.org/datasets/movielens
Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 109–116. ACM, New York (2011). https://doi.org/10.1145/2043932.2043955
Yin, H., Cui, B., Li, J., Yao, J., Chen, C.: Challenging the long tail recommendation. Proc. VLDB Endowment 5(9), 896–907 (2012)
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Coba, L., Symeonidis, P., Zanker, M. (2018). Novelty-Aware Matrix Factorization Based on Items’ Popularity. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science(), vol 11298. Springer, Cham. https://doi.org/10.1007/978-3-030-03840-3_38
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