Abstract
Recommender systems base their operation on past user purchases/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 chapter, we bring to surface factors that affect recommendation process. Moreover, we describe the most important problems related to recommender systems and give some references to actual solutions. Finally, there is an economic and social report regarding recommender systems, which examines them under a rather market-based angle.
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Means \(\overline{r}_{u}\), \(\overline{r}_{v}\) are the mean ratings of u and v over their co-rated items.
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Means \(\overline{r}_{u}\), \(\overline{r}_{v}\) are taken over all ratings of u and v.
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Folding in terms or documents is a simple technique that uses existing SVD to represent new information.
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References
M. Balabanovic, Y. Shoham, Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
M. Berry, S. Dumais, G. O’Brien, Using linear algebra for intelligent information retrieval. SIAM Rev. 37(4), 573–595 (1994)
J. Breese, D. Heckerman, C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, in Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI), Madison, WI (1998), pp. 43–52
R. Burke, Hybrid recommender systems: survey and experiments. User Model. User-adapt. Interact. 12(4), 331–370 (2002)
O. Celma, P. Lamere, Music recommendation tutorial, in International Conference on Music Information Retrieval (ISMIR 2007), Vienna (2007)
M. Deshpande, G. Karypis, Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)
G. Furnas, S. Deerwester, S. Dumais, Information retrieval using a singular value decomposition model of latent semantic structure, in Proceedings of the 13th ACM SIGIR International Conference on Research and Development in Information Retrieval (SIGIR), Grenoble (1988), pp. 465–480
D. Goldberg, D. Nichols, M. Brian, D. Terry, Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
K. Goldberg, T. Roeder, T. Gupta, C. Perkins, Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. 4(2), 133–151 (2001)
J. Herlocker, J. Konstan, A. Borchers, J. Riedl, An algorithmic framework for performing collaborative filtering, in Proceedings of the 22th ACM SIGIR International Conference on Research and Development in Information Retrieval (SIGIR), Berkeley, CA (1999), pp. 230–237
J. Herlocker, J. Konstan, J. Riedl, An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(4), 287–310 (2002)
J. Herlocker, J. Konstan, L. Terveen, J. Riedl, Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
T. Hofmann, Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–115 (2004)
Z. Huang, H. Chen, D. Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22(1), 116–142 (2004)
G. Karypis, Evaluation of item-based top-n recommendation algorithms, in Proceedings of the 10th International Conference on Information and Knowledge Management (CIKM) (2001), pp. 247–254
Y. Koren, Collaborative filtering with temporal dynamics, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Paris (2009), pp. 447–456
Y. Koren, Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)
R. McLauglin, J. Herlocher, A collaborative filtering algorithm and evaluation metric that accurately model the user experience, in Proceedings of the 27th ACM SIGIR International Conference on Research and Development in Information Retrieval (SIGIR), Sheffield (2004), pp. 329–336
B. Mobasher, H. Dai, T. Luo, M. Nakagawa, Improving the effectiveness of collaborative filtering on anonymous web usage data, in Proceedings of the IJCAI Workshop on Intelligent Techniques for Web Personalization (ITWP), Seattle, WA (2001), pp. 53–60
R. Mooney, L. Roy, Content-based book recommending using learning for text categorization, in Proceedings of the 5th ACM Conference on Digital Libraries (DL), San Antonio, TX (2000), pp. 195–204
M. O’Mahony, N. Hurley, N. Kushmerick, G. Silvestre, Collaborative recommendation: a robustness analysis. ACM Trans. Internet Technol. 4(4), 344–377 (2004)
A. Papadimitriou, P. Symeonidis, Y. Manolopoulos, A generalized taxonomy of explanation styles for traditional and social recommender systems. Data Min. Knowl. Discov. 24(3), 555–583 (2012)
M. Pazzani, D. Billsus, Adaptive web site agents. Auton. Agent Multi Agent Syst. 5(2), 205–218 (2002)
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl, Grouplens: an open architecture for collaborative filtering on netnews, in Proceedings of the ACM Conference Computer Supported Collaborative Work (CSCW), Chapel Hill, NC (1994), pp. 175–186
J. Salter, N. Antonopoulos, Cinemascreen recommender agent: combining collaborative and content-based filtering. Intell. Syst. Mag. 21(1), 35–41 (2006)
B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Analysis of recommendation algorithms for e-commerce, in Proceedings of the ACM Conference on Electronic Commerce (EC), Minneapolis, MN (2000), pp. 158–167
B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Application of dimensionality reduction in recommender system - a case study, in Proceedings of the ACM SIGKDD Workshop on Web Mining for E-Commerce - Challenges and Opportunities (WEBKDD), Boston, MA (2000)
B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms, in Proceedings of the 10th International Conference on World Wide Web (WWW), Atlanta, GA (2001), pp. 285–295
B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Incremental singular value decomposition algorithms for highly scalable recommender systems, in Proceedings 5th International Conference on Computer and Information Technology (ICCIT), Dhaka (2002), pp. 27–28
P. Symeonidis, A. Nanopoulos, A. Papadopoulos, Y. Manolopoulos, Scalable collaborative filtering based on latent semantic indexing, in Proceedings of the 21st AAAI Workshop on Intelligent Techniques for Web Personalization (ITWP), Boston, MA (2006), pp. 1–9
P. Symeonidis, A. Nanopoulos, A. Papadopoulos, Y. Manolopoulos, Collaborative recommender systems: combining effectiveness and efficiency. Expert Syst. Appl. 34(4), 2995–3013 (2008)
P. Symeonidis, A. Nanopoulos, Y. Manolopoulos, Moviexplain: a recommender system with explanations, in Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys), New York, NY (2009), pp. 317–320
G. Xue, C. Lin, Q. Yang, W.S. Xi, H.J. Zeng, Y. Yu, Z. Chen, Scalable collaborative filtering using cluster-based smoothing, in Proceedings of the 28th ACM SIGIR International Conference on Research and Development in Information Retrieval (SIGIR), Salvador (2005), pp. 114–121
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Symeonidis, P., Ntempos, D., Manolopoulos, Y. (2014). Recommender Systems. In: Recommender Systems for Location-based Social Networks. SpringerBriefs in Electrical and Computer Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0286-6_2
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DOI: https://doi.org/10.1007/978-1-4939-0286-6_2
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