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.
A. Ragone—Independent Researcher.
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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_17
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_7
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)
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)
Bellogín, A., Sánchez, P.: Revisiting neighbourhood-based recommenders for temporal scenarios. In: Proceedings of TempRec, pp. 40–44 (2017)
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)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of RecSys 2010, pp. 39–46 (2010)
Ding, Y., Li, X.: Time weight collaborative filtering. In: Proceedings of CIKM 2005, pp. 485–492. ACM (2005)
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)
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_8
Jambor, T., Wang, J.: Optimizing multiple objectives in collaborative filtering. In Proceedings of RecSys 2010, pp. 55–62 (2010)
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)
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)
Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)
Lathia, N., Hailes, S., Capra, L.: Temporal collaborative filtering with adaptive neighbourhoods. In: Proceedings of SIGIR 2009, pp. 796–797 (2009)
Liu, N.N., Zhao, M., Xiang, E., Yang, Q.: Online evolutionary collaborative filtering. In: Proceedings of RecSys 2010, pp. 95–102 (2010)
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)
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
Rendle, S., et al.: BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of UAI 2009, pp. 452–461 (2009)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of EC 2000, pp. 158–167 (2000)
Steck, H.: Evaluation of recommendations: rating-prediction and ranking. In: Proceedings of RecSys 2013, pp. 213–220 (2013)
Wu, C., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: Proceedings of WSDM 2017, pp. 495–503 (2017)
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)
Zimdars, A., Chickering, D.M., Meek, C.: Using temporal data for making recommendations. In: Proceedings of UAI 2001, pp. 580–588 (2001)
Rendle, S.: Using temporal data for making recommendations. In: Proceedings of ICDM 2010, pp. 995–1000 (2001)
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Anelli, V.W., Di Noia, T., Di Sciascio, E., Ragone, A., Trotta, J. (2019). Local Popularity and Time in top-N Recommendation. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_63
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