Abstract
Recommender system can help users to effectively identify interested items from a potentially overwhelming huge collection of items, and it has been shown to be very useful in many e-commerce applications. Collaborative filtering (CF), which assumes that similar users may have similar tastes, is one of the most widely used Recommender system techniques. However, one of the major weaknesses for the CF mechanism is the computational cost in computing pairwise similarity of users. This paper attempts to tackle the computational problem of all pairs similarity using the MapReduce technique in the Hadoop framework. We give an overview of our development on using a parallel filtering algorithm to improve the performance of a personal ontology based recommender system for digital library. The experimental results show that the proposed algorithm can indeed scale out the recommender systems for all pairs search.
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References
Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE TKDE, 734–749 (2005)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Avancini, H., Candela, L., Straccia, U.: Recommenders in a personalized, collaborative digital library environment. Journal of Intelligent Information Systems 28(3), 253–283(31) (2007)
Liao, I.-E., Liao, S.-C., Kao, K.-F., Harn, I.-F.: A Personal Ontology Model for Library Recommendation System. In: Sugimoto, S., Hunter, J., Rauber, A., Morishima, A. (eds.) ICADL 2006. LNCS, vol. 4312, pp. 173–182. Springer, Heidelberg (2006)
Liao, I.-E., Hsu, W.-C., Cheng, M.-S., Chen, L.-P.: A library recommender system based on a personal ontology model and collaborative filtering technique for English collections. The Electronic Library 28(3), 386–400 (2010)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. ACM Communication 51(1), 107–113 (2008)
Lin, J.J.: Brute force and indexed approaches to pairwise document similarity comparisons with MapReduce. In: SIGIR 2009, pp. 155–162 (2009)
Elsayed, T., Lin, J., Oard, D.W.: Pairwise Document Similarity in Large Collections with MapReduce. In: Proc. HLT, pp. 265–268 (2008)
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Chen, LC., Kuo, PJ., Liao, IE., Huang, JY. (2013). Scaling Out Recommender System for Digital Libraries with MapReduce. In: Park, J.J.(.H., Arabnia, H.R., Kim, C., Shi, W., Gil, JM. (eds) Grid and Pervasive Computing. GPC 2013. Lecture Notes in Computer Science, vol 7861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38027-3_5
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DOI: https://doi.org/10.1007/978-3-642-38027-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38026-6
Online ISBN: 978-3-642-38027-3
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