Collaborative recommender systems: Combining effectiveness and efficiency
Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the prefere...
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| Published in: | Expert systems with applications Vol. 34; no. 4; pp. 2995 - 3013 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.05.2008
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the “big picture”, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms. |
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| AbstractList | Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the 'information overload' problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the 'big picture', we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms. |
| Author | Papadopoulos, Apostolos N. Symeonidis, Panagiotis Nanopoulos, Alexandros Manolopoulos, Yannis |
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| Keywords | Collaborative filtering Nearest neighbors Recommender system |
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| References | Karypis, G. (2001). Evaluation of item-based top-n recommendation algorithms. In McJones, P., & DeTreville, J. (1997). Each to each programmer’s reference manual. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Huang, Chen, Zeng (bib11) 2004; 22 Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In (pp. 158–167). (pp. 285–295). (pp. 230–237). Mobasher, B., Dai, H., Luo, T., & Nakagawa, M. (2001). Improving the effectiveness of collaborative filtering on anonymous web usage data. In Berry, Dumais, O’Brien (bib1) 1994; 37 Breese, J., Heckerman, D. & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In (pp. 53–60). Xue, G., Lin, C., & Yang, Q., et al. (2005). Scalable collaborative filtering using cluster-based smoothing. In (pp. 329–336). Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: an open architecture for collaborative filtering on netnews. In (pp. 43–52). Herlocker, J., Konstan, J., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Sarwar, B., Karypis, G., Konstan, J., & Riedl J. (2000). Application of dimensionality reduction in recommender system – a case study. In (pp. 114–121). Furnas, G., Deerwester, S. & Dumais, S., et al. (1988). Information retrieval using a singular value decomposition model of latent semantic structure. In Herlocker, Konstan, Terveen, Riedl (bib9) 2004; 22 Hofmann (bib10) 2004; 22 1997-023. . O’Mahony, Hurley, Kushmerick, Silvestre (bib16) 2004; 4 Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2002). Incremental singular value decomposition algorithms for higly scalable recommender systems. In (pp. 465–480). Goldberg, Nichols, Brian, Terry (bib5) 1992; 35 Goldberg, Roeder, Gupta, Perkins (bib6) 2001; 4 Deshpande, Karypis (bib3) 2004; 22 (pp. 247–254). McLauglin, R. & Herlocher, J. (2004). A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In Herlocker, Konstan, Riedl (bib8) 2002; 5 (pp. 175–186). |
| References_xml | – reference: Furnas, G., Deerwester, S. & Dumais, S., et al. (1988). Information retrieval using a singular value decomposition model of latent semantic structure. In – reference: Mobasher, B., Dai, H., Luo, T., & Nakagawa, M. (2001). Improving the effectiveness of collaborative filtering on anonymous web usage data. In – reference: (pp. 285–295). – volume: 35 start-page: 61 year: 1992 end-page: 70 ident: bib5 article-title: Using collaborative filtering to weave an information tapestry publication-title: ACM Communications – reference: Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In – volume: 4 start-page: 133 year: 2001 end-page: 151 ident: bib6 article-title: Eigentaste: a constant time collaborative filtering algorithm publication-title: Information Retrieval – reference: McJones, P., & DeTreville, J. (1997). Each to each programmer’s reference manual. – reference: Breese, J., Heckerman, D. & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In – reference: Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In – reference: , 1997-023. – volume: 37 start-page: 573 year: 1994 end-page: 595 ident: bib1 article-title: Using linear algebra for intelligent information retrieval publication-title: SIAM Review – reference: (pp. 43–52). – volume: 22 start-page: 143 year: 2004 end-page: 177 ident: bib3 article-title: Item-based top-n recommendation algorithms publication-title: ACM Transactions on Information Systems – reference: McLauglin, R. & Herlocher, J. (2004). A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In – reference: Karypis, G. (2001). Evaluation of item-based top-n recommendation algorithms. In – reference: (pp. 465–480). – volume: 5 start-page: 287 year: 2002 end-page: 310 ident: bib8 article-title: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms publication-title: Information Retrieval – volume: 22 start-page: 116 year: 2004 end-page: 142 ident: bib11 article-title: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering publication-title: ACM Transactions on Information Systems – reference: (pp. 175–186). – reference: Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2002). Incremental singular value decomposition algorithms for higly scalable recommender systems. In – reference: (pp. 53–60). – reference: (pp. 230–237). – reference: (pp. 114–121). – reference: (pp. 329–336). – reference: Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: an open architecture for collaborative filtering on netnews. In – reference: (pp. 247–254). – reference: Xue, G., Lin, C., & Yang, Q., et al. (2005). Scalable collaborative filtering using cluster-based smoothing. In: – reference: (pp. 158–167). – reference: Herlocker, J., Konstan, J., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In: – volume: 22 start-page: 89 year: 2004 end-page: 115 ident: bib10 article-title: Latent semantic models for collaborative filtering publication-title: ACM Transactions on Information Systems – reference: Sarwar, B., Karypis, G., Konstan, J., & Riedl J. (2000). Application of dimensionality reduction in recommender system – a case study. In – volume: 4 start-page: 344 year: 2004 end-page: 377 ident: bib16 article-title: Collaborative recommendation: a robustness analysis publication-title: ACM Transactions on Internet Technology – reference: . – volume: 22 start-page: 5 year: 2004 end-page: 53 ident: bib9 article-title: Evaluating collaborative filtering recommender systems publication-title: ACM Transactions on Information Systems |
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| Title | Collaborative recommender systems: Combining effectiveness and efficiency |
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