Abstract
By helping users discover books they may be interested in, recommender systems fully exploit the resources of digital libraries and better facilitate users’ reading demands. Traditional memory-based collaborative filtering (CF) methods are effective and easy to interpret. However, when datasets become larger, the traditional way turns to be infeasible in both time and space. In order to address this challenge, we propose an incremental, cluster-based algorithm on Stream Processing Architecture, which is scalable and suitable to real-time environment. Our experimental results on MovieLens datasets and CADAL user-chapter logs show our algorithm is efficient, while still maintains comparable accuracy and interpretability.
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References
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI 1998, pp. 43–52 (1998)
Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E., Li, H.: Context-aware query suggestion by mining click-through and session data. In: KDD 2008, pp. 875–883 (2008)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys 2010, pp. 39–46 (2010)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)
Giraud-Carrier, C.G.: A note on the utility of incremental learning. AI Commun. 13(4), 215–224 (2000)
Jamali, M., Ester, M.: Using a trust network to improve top-n recommendation. In: RecSys 2009, pp. 181–188 (2009)
Papagelis, M., Rousidis, I., Plexousakis, D., Theoharopoulos, E.: Incremental collaborative filtering for highly-scalable recommendation algorithms. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 553–561. Springer, Heidelberg (2005)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: CSCW 1994, pp. 175–186 (1994)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS 2007 (2007)
Yang, X., Zhang, Z., Wang, K.: Scalable collaborative filtering using incremental update and local link prediction. In: CIKM 2012, pp. 2371–2374 (2012)
Zhang, S., Wang, W., Ford, J., Makedon, F.: Learning from incomplete ratings using non-negative matrix factorization. In: SDM 2006 (2006)
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: SIGMOD 1996, pp. 103–114 (1996)
Zhang, Y., Wang, X., Yu, H., Li, R., Wei, B., Pan, J.: When personalization meets socialization: an icadal approach. In: JCDL 2011, pp. 459–460 (2011)
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Wang, Y., Zhang, Y., Yin, Y., Yi, D., Wei, B. (2013). A Cluster-Based Incremental Recommendation Algorithm on Stream Processing Architecture. In: Urs, S.R., Na, JC., Buchanan, G. (eds) Digital Libraries: Social Media and Community Networks. ICADL 2013. Lecture Notes in Computer Science, vol 8279. Springer, Cham. https://doi.org/10.1007/978-3-319-03599-4_9
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DOI: https://doi.org/10.1007/978-3-319-03599-4_9
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