Advertisement

Novelty-Aware Matrix Factorization Based on Items’ Popularity

  • Ludovik Coba
  • Panagiotis Symeonidis
  • Markus Zanker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)

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.

Keywords

Recommendation algorithms Evaluation Novelty Collaborative filtering Matrix factorization 

References

  1. 1.
    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_27CrossRefGoogle Scholar
  2. 2.
    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_26CrossRefGoogle Scholar
  3. 3.
    Charles, C., et al.: Novelty and diversity in information retrieval evaluation. In: SIGIR Conference, SIGIR 2008, pp. 659–666 (2008)Google Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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/S0306457315000837CrossRefGoogle Scholar
  7. 7.
    Dhillon, I.S., Sra, S.: Generalized nonnegative matrix approximations with Bregman divergences. In: NIPS, pp. 283–290 (2005)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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/10.1145/2827872CrossRefGoogle Scholar
  10. 10.
    Harper, F.M., Konstan, J.A.: The MovieLens datasets. ACM Trans. Interact. Intell. Syst. 5(4), 1–19 (2015)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Jannach, D., Resnick, P., Tuzhilin, A., Zanker, M.: Recommender systems - beyond matrix completion. Commun. ACM 59(11), 94–102 (2016).  https://doi.org/10.1145/2891406CrossRefGoogle Scholar
  14. 14.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 42–49 (2009).  https://doi.org/10.1109/MC.2009.263CrossRefGoogle Scholar
  15. 15.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by nonnegative matrix factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  16. 16.
    Lin, C.J.: On the convergence of multiplicative update algorithms for nonnegative matrix factorization. IEEE Trans. Neural Netw. 18(6), 1589–1596 (2007)CrossRefGoogle Scholar
  17. 17.
    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.abstractMathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    Yin, H., Cui, B., Li, J., Yao, J., Chen, C.: Challenging the long tail recommendation. Proc. VLDB Endowment 5(9), 896–907 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ludovik Coba
    • 1
  • Panagiotis Symeonidis
    • 1
  • Markus Zanker
    • 1
  1. 1.Free University of Bozen-BolzanoBozen-BolzanoItaly

Personalised recommendations