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Local Popularity and Time in top-N Recommendation

  • Vito Walter AnelliEmail author
  • Tommaso Di Noia
  • Eugenio Di Sciascio
  • Azzurra Ragone
  • Joseph Trotta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss an actual personalization, global popularity can be effectively used to recommend items to users. In this paper we introduce the idea of a time-aware personalized popularity in recommender systems by considering both items popularity among neighbors and how it changes over time. An experimental evaluation shows a highly competitive behavior of the proposed approach, compared to state of the art model-based collaborative approaches, in terms of results accuracy.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Multidimensional recommender systems: a data warehousing approach. In: Fiege, L., Mühl, G., Wilhelm, U. (eds.) WELCOM 2001. LNCS, vol. 2232, pp. 180–192. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-45598-1_17CrossRefGoogle Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Boston, MA (2011).  https://doi.org/10.1007/978-0-387-85820-3_7CrossRefzbMATHGoogle Scholar
  3. 3.
    Anelli, V., Di Noia, T., Di Sciascio, E., Lops, P.: Feature factorization for top-n recommendation: from item rating to features relevance. In: Proceedings of RecSysKTL, pp. 16–21 (2017)Google Scholar
  4. 4.
    Bao, H., Li, Q., Liao, S.S., Song, S., Gao, H.: A new temporal and social PMF-based method to predict users’ interests in micro-blogging. Decis. Support Syst. 55(3), 698–709 (2013)CrossRefGoogle Scholar
  5. 5.
    Bellogín, A., Sánchez, P.: Revisiting neighbourhood-based recommenders for temporal scenarios. In: Proceedings of TempRec, pp. 40–44 (2017)Google Scholar
  6. 6.
    Campos, P.G., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. UMAI 24(1–2), 67–119 (2014)Google Scholar
  7. 7.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of RecSys 2010, pp. 39–46 (2010)Google Scholar
  8. 8.
    Ding, Y., Li, X.: Time weight collaborative filtering. In: Proceedings of CIKM 2005, pp. 485–492. ACM (2005)Google Scholar
  9. 9.
    Fernández-Tobías, I., Braunhofer, M., Elahi, M., Ricci, F., Cantador, I.: Alleviating the new user problem in collaborative filtering by exploiting personality information. UMUAI 26(2–3), 221–255 (2016)Google Scholar
  10. 10.
    Gunawardana, A., Shani, G.: Evaluating recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 265–308. Springer, Boston, MA (2015).  https://doi.org/10.1007/978-1-4899-7637-6_8CrossRefGoogle Scholar
  11. 11.
    Jambor, T., Wang, J.: Optimizing multiple objectives in collaborative filtering. In Proceedings of RecSys 2010, pp. 55–62 (2010)Google Scholar
  12. 12.
    Jannach, D., Lerche, L., Gedikli, F., Bonnin, G.: What recommenders recommend - an analysis of accuracy, popularity, and sales diversity effects. In: Proceedings of UMAP 2013, pp. 25–37 (2013)CrossRefGoogle Scholar
  13. 13.
    Jugovac, M., Jannach, D., Lerche, L.: Efficient optimization of multiple recommendation quality factors according to individual user tendencies. Expert Syst. Appl. 81, 321–331 (2017)CrossRefGoogle Scholar
  14. 14.
    Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRefGoogle Scholar
  15. 15.
    Lathia, N., Hailes, S., Capra, L.: Temporal collaborative filtering with adaptive neighbourhoods. In: Proceedings of SIGIR 2009, pp. 796–797 (2009)Google Scholar
  16. 16.
    Liu, N.N., Zhao, M., Xiang, E., Yang, Q.: Online evolutionary collaborative filtering. In: Proceedings of RecSys 2010, pp. 95–102 (2010)Google Scholar
  17. 17.
    Oh, J., Park, S., Yu, H., Song, M., Park, S.: Novel recommendation based on personal popularity tendency. In: Proceedings of ICDM 2011, pp. 507–516 (2011)Google Scholar
  18. 18.
    Rendle, S.: Factorization machines. In: Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) The 10th IEEE International Conference on Data Mining, ICDM 2010, Sydney, Australia, 14–17 December 2010, pp. 995–1000. IEEE Computer Society (2010).  https://doi.org/10.1109/ICDM.2010.127, http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5690658
  19. 19.
    Rendle, S., et al.: BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of UAI 2009, pp. 452–461 (2009)Google Scholar
  20. 20.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of EC 2000, pp. 158–167 (2000)Google Scholar
  21. 21.
    Steck, H.: Evaluation of recommendations: rating-prediction and ranking. In: Proceedings of RecSys 2013, pp. 213–220 (2013)Google Scholar
  22. 22.
    Wu, C., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: Proceedings of WSDM 2017, pp. 495–503 (2017)Google Scholar
  23. 23.
    Xia, C., Jiang, X., Liu, S., Luo, Z., Yu, Z.: Dynamic item-based recommendation algorithm with time decay. In: Proceedings of ICNC 2010, pp. 242–247 (2010)Google Scholar
  24. 24.
    Zimdars, A., Chickering, D.M., Meek, C.: Using temporal data for making recommendations. In: Proceedings of UAI 2001, pp. 580–588 (2001)Google Scholar
  25. 25.
    Rendle, S.: Using temporal data for making recommendations. In: Proceedings of ICDM 2010, pp. 995–1000 (2001)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vito Walter Anelli
    • 1
    Email author
  • Tommaso Di Noia
    • 1
  • Eugenio Di Sciascio
    • 1
  • Azzurra Ragone
    • 2
  • Joseph Trotta
    • 1
  1. 1.Polytechnic University of BariBariItaly
  2. 2.BariItaly

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