Abstract
In this paper, we develop a collaborative filtering system for not only tackling the sparsity problem by exploiting community context information but for also dealing with data imperfections by means of Dempster-Shafer theory. The experimental results show that the proposed system achieves better performance when comparing it with a similar system, CoFiDS.
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
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Bloch, I.: Some aspects of dempster-shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recognition Letters 17(8), 905–919 (1996)
Chan, H., Darwiche, A.: A distance measure for bounding probabilistic belief change. Int. J. Approx. Reasoning 38(2), 149–174 (2005)
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics 38, 325–339 (1967)
Durrant-Whyte, H.F., Henderson, T.C.: Multisensor data fusion. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 585–610. Springer (2008)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, August 15-19, pp. 230–237. ACM (1999)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Hewawasam, K.K.R., Premaratne, K., Shyu, M.L.: Rule mining and classification in a situation assessment application: A belief-theoretic approach for handling data imperfections. IEEE Transactions on Systems, Man, and Cybernetics, Part B 37(6), 1446–1459 (2007)
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), 116–142 (2004)
Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: A review of the state-of-the-art. Information Fusion 14(1), 28–44 (2013)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Computer 42(8), 30–37 (2009)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.B. (eds.): Recommender Systems Handbook. Springer (2011)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)
Smets, P.: Practical uses of belief functions. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, UAI 1999, pp. 612–621. Morgan Kaufmann Publishers Inc. (1999)
Wickramarathne, T.L., Premaratne, K., Kubat, M., Jayaweera, D.T.: Cofids: A belief-theoretic approach for automated collaborative filtering. IEEE Trans. Knowl. Data Eng. 23(2), 175–189 (2011)
Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS(LNAI), vol. 7302, pp. 25–36. Springer, Heidelberg (2012)
Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3–28 (1978)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nguyen, VD., Huynh, VN. (2014). A Community-Based Collaborative Filtering System Dealing with Sparsity Problem and Data Imperfections. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_74
Download citation
DOI: https://doi.org/10.1007/978-3-319-13560-1_74
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
eBook Packages: Computer ScienceComputer Science (R0)