Abstract
The task of collaborative filtering is to predict the preferences of an active user for unseen items given preferences of other users. These preferences are typically expressed as numerical ratings. In this paper, we propose a novel regression-based approach that first learns a number of experts describing relationships in ratings between pairs of items. Based on ratings provided by an active user for some of the items, the experts are combined by using statistical methods to predict the user’s preferences for the remaining items. The approach was designed to efficiently address the problem of data sparsity and prediction latency that characterise collaborative filtering. Extensive experiments on Eachmovie and Jester benchmark collaborative filtering data show that the proposed regression-based approach achieves improved accuracy and is orders of magnitude faster than the popular neighbour-based alternative. The difference in accuracy was more evident when the number of ratings provided by an active user was small, as is common for real-life recommendation systems. Additional benefits were observed in predicting items with large rating variability. To provide a more detailed characterisation of the proposed algorithm, additional experiments were performed on synthetic data with second-order statistics similar to that of the Eachmovie data. Strong experimental evidence was obtained that the proposed approach can be applied to data over a large range of sparsity scenarios and is superior to non-personalised predictors even when ratings data are very sparse.
Similar content being viewed by others
References
Aggrawal CC, Wolf JL, Wu K, Yu PS (1999) Horting hatches an egg: a new graph-theoretic approach to collaborative filtering. In: Proceedings, ACM Knowledge Discovery in Databases Conference, pp 201–212
Bates JM, Granger CWJ (1969) The combination of forecasts. Oper Res Q 20:451–468
Billsus D, Pazzani MJ (1998) Learning collaborative information filters. In: Proceedings, Fifteenth International Conference on Machine Learning, pp 46–54
Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, pp 43–52
Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper. In: Proceedings, ACM SIGIR Workshop on Recommender Systems
Delgado J, Ishii N, Ura T (1998) Content-based collaborative information filtering: actively learning to classify and recommend documents. In: Klush M, Weiss G (eds) Cooperative Agents II, Proceedings/CIA’98, LNAI Series Vol 1435. Springer-Verlag, Heidelberg, pp 206–215
Freund Y, Iyer R, Schapire R, Singer Y (1998) An efficient boosting algorithm for combining preferences. In: Shavlik J (ed) Proceedings of the Fifteenth International Conference in Machine Learning, pp 170–178
Goldberg K, Roeder T, Gupta D, Perkins, K (2001) Eigentaste: a constant-time collaborative filtering algorithm. Inf Retrieval 4(2):133–151
Greening D (1997) Building customer trust with accurate product recommendations. LikeMinds White Paper LMWSWP-210-6966
Griffiths WE, Hill RC, Judge GG (1993) Learning and Practicing Econometrics. John Wiley & Sons
Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings, 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 230–237
Hoffman T, Puzicha J (1999) Latent class models for collaborative filtering. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence
Karypis G (2001) Evaluation of item-based Top-N recommendation algorithms. In: Proceedings of the 10th Conference of Information and Knowledge Management
Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J (1997) GroupLens: applying collaborative filtering to Usenet news. Commun ACM 40(3):77–87
Lin WY, Alvarez SA, Ruiz C (2002) Efficient adaptive-support association rule mining for recommender systems. Data Min Knowl Discovery 6(1):83–105
Linden GD, Jacobi JA, Benson EA (2001) Collaborative recommendations using item-to-item similarity mappings. US Patent 6 266 649
Maes P (1994) Agents that reduce work and information overload. Commun ACM 37(7):30–40
McJones P (1997) EachMovie collaborative filtering data set. DEC Systems Research Center, http://www.research.digital.com/SRC/eachmovie/
Merz CJ, Pazzani MJ (1999) A principal components approach to combining regression estimates. Mach Learn 36(1–2):9–32
Nakamura A, Abe N (1998) Collaborative filtering using weighted majority prediction algorithms. In: Proceedings, 15th International Conference on Machine Learning, pp 395–403
Newbold P, Granger CWJ (1974) Experience with forecasting univariate time series and the combination of forecasts. J R Stat Soc Ser A 137:131–146
Pennock DM, Horvitz E, Lawrence S, Giles LC (2000) Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach. In: Proceedings, 16th Conference on Uncertainty in Artificial Intelligence, pp 473–480
Pine BJ (1993) Mass Customization. Harvard Business School Press, Boston, MA
Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings, IEEE International Conference on Neural Networks, pp 586–591
Salton G, Buckley C (1998) Term-weighting approaches in automatic text retrieval. Inf Process Manage 24(5):513–523
Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings Computer Human Interaction Conference, pp 210–217
Ungar LH, Foster DP (1998) Clustering methods for collaborative filtering. In: Proceedings, Workshop on Recommendation Systems. AAAI Press, Menlo Park, CA
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Vucetic, S., Obradovic, Z. Collaborative Filtering Using a Regression-Based Approach. Know. Inf. Sys. 7, 1–22 (2005). https://doi.org/10.1007/s10115-003-0123-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-003-0123-8