Recommender systems

By Paul Resnick, Hal R. Varian. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

An MDP-Based Recommender System (2002, positive): “Recommender Systems (Resnick and Varian, 1997), help users limit their search by supplying a list of items that might interest a specific user.”
Applying Common Identity and Bond Theory to Design of Online Communities (2007, background): “For example, in both netflix.com and movielens.org, two social recommender sites (Resnick and Varian 1997), subscribers’ judgments of movies they have seen provide the basis for the site’s making recommendations to others.”
Making sense of recommendations (2019, background): “Most notably, recommender systems now predict which movies and books people will enjoy, which products they should buy, and which restaurants they should visit [2].”
Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration (2018, background): “Recommender systems help people navigate a large space of options [33] and can both increase user satisfaction as well as achieve other business goals [16].”
PolyLens: A recommender system for groups of user (2001, background): “Recommender systems (Resnick & Varian, 1997) help users faced with an overwhelming selection of items by identifying particular items that are likely to match each user’s tastes or preferences (Schafer et al., 1999).”
Matchbox: large scale online bayesian recommendations (2009, unknown): “Automatic recommender systems can aid users in this selection [7, 19], some of the best known being found at Amazon, [18], Netflix [1], and Yahoo!.”
Beyond Bowling Together: SocioTechnical Capital (2001, unknown): “Recommender systems ( Resnick and Varian 1997 ) can supercharge this process, allowing recommendation sharing among people who may not know each other or be explicitly aware of each other's interests.”
Progress in information technology and tourism management: 20 years on and 10 years after the Internet - the state of eTourism research. (2008, methodology): “A recommender system is to provide assistance in the social process of indicating or receiving indications about what options are better suited in a specific case for specific individuals (Gretzel, Mitsche, Hwang, & Fesenmaier, 2004; Resnick & Varian, 1997).”
Recommender Systems: Introduction and Challenges (2015, background): “Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user [17, 41, 42].”
Empirical Analysis of Predictive Algorithms for Collaborative Filtering (1998, methodology): “Results indicate that for a wide range of con ditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector similarity methods.”