Collaborative Filtering with Maximum Entropy

IEEE Intelligent SystemsPublished 1 November 2004

D. Pavlov, Eren Manavoglu, D.M. Pennock, C. Lee Giles

DOI10.1109/mis.2004.59

Source URL

Citations31

SJR quartileQ1

SJR score1.33

SNIP2.01

TL;DR

A novel maximum-entropy algorithm for generating accurate recommendations and a data-clustering approach for speeding up model training are presented.

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

Computer Science

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