Abstract
Recommender systems heavily rely on the availability of historical user preference data, struggling to provide relevant suggestions for new users. The cold start user scenario is thus recognized as one of the most challenging problems in the recommender systems research area. Previous work has focused on exploiting additional information about users and items –e.g., user personality and item metadata– to mitigate the lack of user feedback. However, it is still unclear how to approach the worst scenario where no side information is available to a recommender system. Addressing this problem, in this paper we focus on new users of memory-based collaborative filtering methods with positive-only feedback, and conduct a comprehensive study of a number of neighbor selection strategies. Specifically, we present empirical results on several datasets analyzing the effects of choosing adequately the user similarity, the set of candidate neighbors, and the size of the user neighborhoods. In particular, we show that even few but reliable neighbors lead to better recommendations than large neighborhoods where cold start users belong to.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barjasteh, I., Forsati, R., Masrour, F., Esfahanian, A.H., Radha, H.: Cold-start item and user recommendation with decoupled completion and transduction. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, pp. 91–98. ACM, New York (2015). https://doi.org/10.1145/2792838.2800196
Bellogín, A., Castells, P., Cantador, I.: Neighbor selection and weighting in user-based collaborative filtering: a performance prediction approach. ACM Trans. Web 8(2), 12:1–12:30 (2014). https://doi.org/10.1145/2579993
Dunning, T.: Accurate methods for the statistics of surprise and coincidence. Comput. Linguist. 19(1), 61–74 (1993)
Enrich, M., Braunhofer, M., Ricci, F.: Cold-start management with cross-domain collaborative filtering and tags. In: Huemer, C., Lops, P. (eds.) EC-Web 2013. LNBIP, vol. 152, pp. 101–112. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39878-0_10
Fernández-Tobías, I., Braunhofer, M., Elahi, M., Ricci, F., Cantador, I.: Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model. User-Adap. Inter. 26(2–3), 221–255 (2016). https://doi.org/10.1007/s11257-016-9172-z
Gantner, Z., Drumond, L., Freudenthaler, C., Rendle, S., Schmidt-Thieme, L.: Learning attribute-to-feature mappings for cold-start recommendations. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM 2010, pp. 176–185. IEEE Computer Society, Washington, DC (2010). https://doi.org/10.1109/ICDM.2010.129
Graus, M.P., Willemsen, M.C.: Improving the user experience during cold start through choice-based preference elicitation. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, pp. 273–276. ACM, New York (2015). https://doi.org/10.1145/2792838.2799681
Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retrieval 5(4), 287–310 (2002). https://doi.org/10.1023/A:1020443909834
Kluver, D., Konstan, J.A.: Evaluating recommender behavior for new users. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014, pp. 121–128. ACM, New York (2014). https://doi.org/10.1145/2645710.2645742
Ning, X., Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 37–76. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_2
Tomeo, P., Fernández-Tobías, I., Cantador, I., Noia, T.D.: Addressing the cold start with positive-only feedback through semantic-based recommendations. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 25(Suppl.-2), 57–78 (2017). https://doi.org/10.1142/S0218488517400116
Acknowledgments
This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Bellogín, A., Fernández-Tobías, I., Cantador, I., Tomeo, P. (2018). Neighbor Selection for Cold Users in Collaborative Filtering with Positive-Only Feedback. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-00374-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00373-9
Online ISBN: 978-3-030-00374-6
eBook Packages: Computer ScienceComputer Science (R0)