Abstract
Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as “similar” according to their logged history of prior transactions. However, the applicability of CF is limited due to the sparsity problem, which refers to a situation that transactional data are lacking or are insufficient. In an attempt to provide high-quality recommendations even when data are sparse, we propose a method for alleviating sparsity using trust inferences. Trust inferences are transitive associations between users in the context of an underlying social network and are valuable sources of additional information that help dealing with the sparsity and the cold-start problems. A trust computational model has been developed that permits to define the subjective notion of trust by applying confidence and uncertainty properties to network associations. We compare our method with the classic CF that does not consider any transitive associations. Our experimental results indicate that our method of trust inferences significantly improves the quality performance of the classic CF method.
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
Preview
Unable to display preview. Download preview PDF.
References
Resnick, P., Varian Hal, R.: Recommender Systems (introduction to special section). Communications of the ACM 40(3) (1997)
Herlocker, J., Konstan, J.A., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1) (2004)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining Collaborative Filtering Recommendations. In: Proc. of the ACM Conference on Computer Supported Cooperative Work (2002)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proc. of ACM Electronic Commerce (2000)
Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing (January 2003)
Schein, A.I., Popescul, A., Ungar, L.H.: Methods and Metrics for Cold-Start Recommendations. In: Proc. of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2002)
Goldbergh, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Inf. Ret. 4(2), 133–151 (2001)
Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proc. of the 15th International Conference on Machine Learning, pp. 46–54 (1998)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. JASIS 41(6) (1990)
Hofmann, T.: Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis. In: Proc. of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2003)
Huang, Z., Chen, H., Zeng, D.: Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering. ACM Transactions on Information Systems 22(1) (2004)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proc. of WWW (2001)
Popescul, A., Ungar, L.H., Pennock, D.M., Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. In: Proc. of UAI (2001)
Melville, P., Mooney, R.J., Nagaragan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. In: Proc. of the National Conference of the American Association Artificial Intelligence (2002)
Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating word of mouth. In: Proc. of CHI, 1995 – Human Factors in Computing Systems, pp. 210–217 (1995)
Allen, R.B.: User Models: Theory, method and Practice. International Journal of Man-Machine Studies (1990)
Ziegler, C.N., Lausen, G.: Analyzing Correlation Between Trust and User Similarity in Online Communities. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 251–265. Springer, Heidelberg (2004)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the 14th Conference on Uncertainty in Artificial Intelligence (1998)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proc. of the 22nd ACM SIGIR Conference on Research and Development in Information Retrieval (1999)
Pearson, K.: Mathematical contribution to the theory of evolution: VII, on the correlation of characters not quantitatively measurable. Phil. Trans. R. Soc. Lond. A 195, 1–47 (1900)
Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of Trust and Distrust. In: Proc. of WWW 2004 (2004)
Ziegler, C.N., Lausen, G.: Spreading Activation Models for Trust Propagation. In: Proc. of IEEE International Conference on e-Technology, e-Commerce, and e-Service (2004)
Josang, A.: A Logic for Uncertain Probabilities. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9(3) (June 2001)
Faloutsos, M., Faloutsos, P., Faloutsos, C.: On Power-Law Relationships of the Internet Topology. In: Proc. of SIGCOMM (1999)
Papagelis, M., Plexousakis, D.: Qualitative Analysis of User-based and Item-based Prediction Algorithms for Recommendation Agents. International Journal of Engineering Applications of Artificial Intelligence 18(4) (June 2005)
Papagelis, M., Rousidis, I., Plexousakis, D., Theoharopoulos, E.: Incremental Collaborative Filtering for Highly-Scalable Recommendation algorithms. In: Proc. of International Symposium on Methodologies of Intelligent Systems (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Papagelis, M., Plexousakis, D., Kutsuras, T. (2005). Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences. In: Herrmann, P., Issarny, V., Shiu, S. (eds) Trust Management. iTrust 2005. Lecture Notes in Computer Science, vol 3477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11429760_16
Download citation
DOI: https://doi.org/10.1007/11429760_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26042-4
Online ISBN: 978-3-540-32040-1
eBook Packages: Computer ScienceComputer Science (R0)