UMBC Institute for Global Electronic Commerce

ecTechWeb: TECHNOLOGY: Recommender systems

Algorithms for Collaborative Filtering 0 - In this paper we describe
several algorithms designed for this task, including techniques based on correlation
coefficients, vector-based similarity calculations, and statistical Bayesian methods. We
compare the predictive accuracy of the various methods in a set of representative
problem domains.
GroupLens 0 - University of Minnesota research projects related to the
fields of information filtering, collaborative filtering, and
recommender systems.
PHOAKS 0 - People post their opinions of web resources in Usenet
Netnews (you can too). Around the clock, PHOAKS reads, classifies, abstracts and
tallies those opinions automatically.
SiteSeer! 0 - SiteSeer delivers you a personalized view of the web, driven by your own bookmarks.
SiteSeer finds other surfers who share your same interests, and uses their
bookmarks to recommend sites to visit
Jester: The On-Line Joke Recommender 0 - Jester uses statistical techniques (collaborative filtering) to
recommend jokes based on your ratings of a set of sample jokes.
Jester 2.0 is an improved system (released 1st March 1999) based
on analysis of ratings by 7000 users of Jester 1.0. The new version
gives faster recommendations using principle component analysis,
clustering, and b-tree data structures.
Active Web Museum 0 - The Active WebMuseum is a research recommender system for art
paintings that allows you to browse through paintings in various
directions (painter, century, color, texture). The tour is
personalized by the use of collaborative filtering, i.e. other
visitors who have similar preferences/tastes are automatically used as
recommenders.
PersonaLogic, Inc. 0 - Develops guides to help consumers make decisions about products. Now a division of AOL.

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