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Recommended Systems Resource Center


Recommender System Algorithms
"Semantic Web Interaction through Trust..."
Paper: "Semantic Web Interaction through Trust Network Recommender Systems, by Jennifer Golbeck of the University of Maryland. Discusses FilmTrust—a site that uses trust in Semantic Web-based social networks to provide movie recommendations. Provides an introduction to the FilmTrust site and services, then discusses how the site provides personalization features including computing recommended movie ratings, determining the accuracy of recommended ratings, and presenting ordered reviews.
"Analysis of Recommender Systems’ Algorithms"
Paper: "Analysis of Recommender Systems’ Algorithms" by Emmanouil Vozalis and Konstantinos G. Margaritis. Provides an introduction to recommender systems' algorithms, and challenges and problems with recommenders systems. Topics include association rules, memory based algorithms, model-based algorithms, hybrid recommendation algorithms, evaluation metrics for measuring performance and more.
"Item-based Collaborative Filtering..."
Paper: "Item-based Collaborative Filtering Recommendation Algorithms" by Badrul Sarwar, George Karypis, Joseph Konstan and John Riedl, all of the GroupLens Research Group at the University of Minnesota. Topics include collaborative filtering based recommender systems, item-based collaborative filtering algorithm and experimental evaluation.
"Explanatory Algorithms"
Blog entry: "Explanatory Algorithms" by Cameron Marlow, a research scientist at Yahoo (November 9th, 2006). Discusses how several popular recommender systems (including Amazon, iLike and even Google) are revealing more about their recommendation algorithms, which in turns helps to build trust.
ACM SIGIR'99 Workshop
ACM SIGIR'99 Workshop on Recommender Systems: Algorithms and Evaluation. Download the papers that were presented at the workshop including: "Memory-Based Weighted-Majority Prediction for Recommender Systems," by Joaquin Delgado and Naohiro Ishii; "Jester 2.0: A New Linear-Time Collaborative Filtering Algorithm Applied to Jokes," by Dhruv Gupta, Mark Digiovanni, Hiro Narita, and Ken Goldberg; "Clustering Items for Collaborative Filtering," by Mark O'Conner and Jon Herlocker; "Combining Content-Based and Collaborative Filters in an Online Newspaper," by Mark Claypool, Anuja Gokhale, Tim Miranda, Pavel Murnikov, Dmitry Netes, and Matthew Sartin; "Bayesian Mixed-Effect Models for Recommender Systems," by Michelle Condliff, David D. Lewis, David Madigan, and Christian Posse; "Content-Based Book Recommending Using Learning for Text Categorization." by Raymond Mooney and Loriene Roy; "Recommenders for Expertise Management," by Mark Ackerman, David McDonald, Wayne Lutters, and Jack Mauamatsu; "Recommending Web Documents Based on User Preferences," by Eric Glover, Steve Lawrence, Michael Grodon, William Birmingham, and C. Lee Giles.

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Update :: December 15, 2019