Abstract
Language Models are state-of-the-art methods in Information Retrieval. Their sound statistical foundation and high effectiveness in several retrieval tasks are key to their current success. In this paper, we explore how to apply these models to deal with the task of computing user or item neighbourhoods in a collaborative filtering scenario. Our experiments showed that this approach is superior to other neighbourhood strategies and also very efficient. Our proposal, in conjunction with a simple neighbourhood-based recommender, showed a great performance compared to state-of-the-art methods (NNCosNgbr and PureSVD) while its computational complexity is low.
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
Bellogín, A., Castells, P., Cantador, I.: Precision-oriented evaluation of recommender systems. In: RecSys 2011, p. 333. ACM (2011)
Bellogín, A., Parapar, J., Castells, P.: Probabilistic collaborative filtering with negative cross entropy. In: RecSys 2013, pp. 387–390. ACM (2013)
Bellogín, A., Wang, J., Castells, P.: Bridging memory-based collaborative filtering and text retrieval. Inf. Retr. 16(6), 697–724 (2013)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-N recommendation tasks. In: RecSys 2010, pp. 39–46. ACM (2010)
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)
Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Heidelberg (2011)
Fleder, D., Hosanagar, K.: Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity. Manage. Sci. 55(5), 697–712 (2009)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., John, T.: Riedl.: evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008, pp. 426–434. ACM (2008)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 145–186. Springer, Heidelberg (2011)
Losada, D.E., Azzopardi, L.: An analysis on document length retrieval trends in language modeling smoothing. Inf. Retr. 11(2), 109–138 (2008)
Losada, D.E., Azzopardi, L.: Assessing multivariate bernoulli models for information retrieval. ACM Trans. Inf. Syst. 26(3), 17:1–17:46 (2008)
Parapar, J., Bellogín, A., Castells, P., Barreiro, Á.: Relevance-based language modelling for recommender systems. Inf. Process. Manage. 49(4), 966–980 (2013)
Ponte, J.M., Bruce Croft, W.: A language modeling approach to information retrieval. In: SIGIR 1998, pp. 275–281. ACM (1998)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Heidelberg (2011)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
Valcarce, D.: Exploring statistical language models for recommender systems. In: RecSys 2015, pp. 375–378. ACM (2015)
Valcarce, D., Parapar, J., Barreiro, Á.: A study of priors for relevance-based language modelling of recommender systems. In: RecSys 2015, pp. 237–240. ACM (2015)
Valcarce, D., Parapar, J., Barreiro, A.: A study of smoothing methods for relevance-based language modelling of recommender systems. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 346–351. Springer, Heidelberg (2015)
Wang, J., de Vries, A.P., Reinders, M.J.T.: A user-item relevance model for log-based collaborative filtering. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 37–48. Springer, Heidelberg (2006)
Wang, Y., Wang, L., Li, Y., He, D., Chen, W., Liu, T.-Y.: A theoretical analysis of NDCG ranking measures. In: COLT 2013, pp. 1–30 (2013). JMLR.org
Zhai, C.: Statistical Language Models for Information Retrieval. Synthesis Lectures on Human Language Technologies. Morgan & Claypool, San Rafael (2009)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22(2), 179–214 (2004)
Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J.R., Zhang, Y.-C.: Solving the apparent diversity-accuracy dilemma of recommender systems. PNAS 107(10), 4511–4515 (2010)
Acknowledgments
This work was supported by the Ministerio de Economía y Competitividad of the Goverment of Spain under grants TIN2012-33867 and TIN2015-64282-R. The first author also wants to acknowledge the support of Ministerio de Educación, Cultura y Deporte of the Government of Spain under the grant FPU014/01724.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Valcarce, D., Parapar, J., Barreiro, Á. (2016). Language Models for Collaborative Filtering Neighbourhoods. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-30671-1_45
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30670-4
Online ISBN: 978-3-319-30671-1
eBook Packages: Computer ScienceComputer Science (R0)