Collaborative Filtering Recommender Systems

Collaborative Filtering Recommender Systems (2007)

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by J. Ben Schafer , Dan Frankowski , Jon Herlocker , Shilad Sen

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@MISC{Schafer07collaborativefiltering,
    author = {J. Ben Schafer and Dan Frankowski and Jon Herlocker and Shilad Sen},
    title = { Collaborative Filtering Recommender Systems},
    year = {2007}
}

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Abstract

One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.

Citations

2168	Indexing by latent semantic analysis - Deerwester, Dumais, et al. - 1990
778	Em-pirical Analysis of Predictive Algorithms for Col-laborative Filtering - Breese, Heckerman, et al. - 1998
577	Using collaborative filtering to weave an information tapestry - Goldberg, Nichols, et al. - 1992
390	An algorithmic framework for performing collaborative Filtering - Herlocker, Konstan, et al. - 1999
381	A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions - Adomavicius, Tuzhilin
380	Fab: Content-Based, Collaborative Recommendation - Balabanovic, Shoham - 1997
368	Evaluating collaborative filtering recommender systems - Herlocker, Konstan, et al. - 2004
229	G.: Recommending and evaluating choices in a virtual community of use - Hill, Stead, et al. - 1995
207	Learning Collaborative Information Filters - Billsus, MJ - 1998
206	Explaining collaborative filtering recommendations - Herlocker, Konstan, et al. - 2000
200	W.: Recommendation as classification: Using social and content-based information in recommendation - Basu, Hirsh, et al. - 1998
178	Combininig collaborative filtering with personal agents for better recommendations - Good, Schafer, et al. - 1999
174	Edit wear and read wear - Hill, Hollan, et al. - 1992
168	Hierarchical clustering schemes - Johnson
147	Latent semantic models for collaborative filtering - Hofmann - 2000
127	Combining content-based and collaborative filters in an online newspaper - Claypool, Gokhale, et al. - 1999
122	K.: Dependency Networks for Inference, Collaborative Filtering, and Data Visualization - Heckerman, Chickering, et al.
103	Collaborative Filtering with Privacy via Factor Analysis - CANNY
87	Evaluation of item-based top-N recommendation algorithms - Karypis - 2001
77	P.S.: Horting Hatches an Egg: A New GraphTheoretic Approach to Collaborative Filtering - Aggarwal, Wolf, et al. - 1999
47	The Market for evaluations - Avery, Resnick, et al. - 1999
34	Bayesian mixed-effects models for recommender systems - Condliff, Lewis, et al. - 1999
21	Is seeing believing?: how recommender system interfacesaffectusers’opinions - Cosley, Lam, et al.
17	You are what you say: privacy risks of public mentions - Frankowski, Cosley, et al. - 2006
15	Jump-starting movielens: User benefits of starting a collaborative filtering system with “dead-data - Dahlen, Konstan, et al. - 1998
15	An Economic Model of User Rating in an Online Recommender System - Harper, Li, et al. - 2005
13	Footprints in the snow - Munro, Hook, et al. - 1999
10	G.W.: Using Linear Algebra for Intelligent Information Retrieval - Berry, Dumais, et al. - 1995
9	Privacy-Enhanced Web Personalization - Kobsa - 2007
7	Eigentaste: A Constant-Time Collaborative Filtering Algorithm - Goldberg, Roeder, et al. - 2001
5	N.: Memory-Based Weighted Majority Prediction for Recommender Systems - Delgado - 1999
1	Hybrid Web Recomender Systems - Burke - 2007