Title :
Collaborative filtering with maximum entropy
Author :
Pavlov, Dmitry ; Manavoglu, Eren ; Giles, C. Lee ; Pennock, David M.
Abstract :
As users navigate through online document collections on high-volume Web servers, they depend on good recommendations. We present a novel maximum-entropy algorithm for generating accurate recommendations and a data-clustering approach for speeding up model training. Recommender systems attempt to automate the process of "word of mouth" recommendations within a community. Typical application environments such as online shops and search engines have many dynamic aspects.
Keywords :
Internet; document handling; information filtering; information filters; maximum entropy methods; search engines; Web servers; collaborative filtering; data-clustering approach; maximum entropy algorithm; online document collection; recommender systems; search engines; Bayesian methods; Collaboration; Collaborative work; Computer science; Context modeling; Entropy; Filtering; History; Navigation; Search engines; maximum entropy model; mixture models; recommender systems; sequence modeling;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2004.59
