Enhancing New User Cold-Start Based on Decision Trees Active Learning by Using Past Warm-Users Predictions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

The cold-start is the situation in which the recommender system has no or not enough information about the (new) users/items, i.e. their ratings/feedback; hence, the recommendations are not accurate. Active learning techniques for recommender systems propose to interact with new users by asking them to rate sequentially a few items while the system tries to detect her preferences. This bootstraps recommender systems and alleviate the new user cold-start. Compared to current state of the art, the presented approach takes into account the users’ ratings predictions in addition to the available users’ ratings. The experimentation shows that our approach achieves better performance in terms of precision and limits the number of questions asked to the users.

Keywords

Active learning for recommender systems Cold-start problem New users problem Decision trees 

Notes

Acknowledgments

This work has been supported by FIORA project, and funded by “DGCIS” and “Conseil Regional de l’Île de France”.

References

  1. 1.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)CrossRefGoogle Scholar
  2. 2.
    Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 595–604. ACM (2011)Google Scholar
  3. 3.
    Rubens, N., Kaplan, D., Sugiyama, M.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 735–767. Springer, Boston (2011). doi: 10.1007/978-0-387-85820-3_23CrossRefGoogle Scholar
  4. 4.
    Karimi, R., Freudenthaler, C., Nanopoulos, A., Schmidt-Thieme, L.: Comparing prediction models for active learning in recommender systems. In: Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB (2015). http://ceur-ws.org
  5. 5.
    Pilászy, I., Tikk, D.: Recommending new movies: even a few ratings are more valuable than metadata. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 93–100. ACM (2009)Google Scholar
  6. 6.
    Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, pp. 127–134. ACM (2002)Google Scholar
  7. 7.
    Elahi, M., Ricci, F., Rubens, N.: Active learning in collaborative filtering recommender systems. In: Hepp, M., Hoffner, Y. (eds.) EC-Web 2014. LNBIP, vol. 188, pp. 113–124. Springer, Cham (2014). doi: 10.1007/978-3-319-10491-1_12CrossRefGoogle Scholar
  8. 8.
    Hofmann, T.: Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 259–266. ACM (2003)Google Scholar
  9. 9.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: SDM, vol. 5, SIAM 1–5 (2005)CrossRefGoogle Scholar
  10. 10.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  11. 11.
    Peis, E., del Castillo, J.M., Delgado-López, J.: Semantic recommender systems. Analysis of the state of the topic. Hipertext.net 6, 1–5 (2008)Google Scholar
  12. 12.
    Ziegler, C.N., Lausen, G., Schmidt-Thieme, L.: Taxonomy-driven computation of product recommendations. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 406–415. ACM (2004)Google Scholar
  13. 13.
    Vozalis, M.G., Margaritis, K.G.: Using SVD and demographic data for the enhancement of generalized collaborative filtering. Inf. Sci. 177(15), 3017–3037 (2007)CrossRefGoogle Scholar
  14. 14.
    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, pp. 91–98. ACM (2015)Google Scholar
  15. 15.
    Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explor. Newsl. 10(2), 90–100 (2008)CrossRefGoogle Scholar
  16. 16.
    Zhou, K., Yang, S.H., Zha, H.: Functional matrix factorizations for cold-start recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 315–324. ACM (2011)Google Scholar
  17. 17.
    Karimi, R., Nanopoulos, A., Schmidt-Thieme, L.: A supervised active learning framework for recommender systems based on decision trees. User Model. User Adapt. Interact. 25(1), 39–64 (2015)CrossRefGoogle Scholar
  18. 18.
    Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the Netflix prize. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-68880-8_32CrossRefGoogle Scholar
  19. 19.
    Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1–19:19 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institut Supérieur d’Eléctronique de Paris, LISITE LabParisFrance
  2. 2.BlackpillsParisFrance
  3. 3.Informatics Research InstituteUniversity of SalfordSalfordUK
  4. 4.CEDRIC Lab, CNAMParisFrance

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