Collaborative filtering with maximum entropy

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 au...

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Bibliographic Details
Published in:IEEE intelligent systems 2004-11, Vol.19 (6), p.40-47
Main Authors: Pavlov, D., Manavoglu, E., Giles, C.L., Pennock, D.M.
Format: Article
Language:English
Subjects:
Algorithms
Bayesian methods
Collaboration
Collaborative work
Collection
Communities
Computer science
Context modeling
Dynamical systems
Dynamics
Entropy
Filtering
History
Maximum entropy
maximum entropy model
mixture models
Navigation
On-line systems
Online
recommender systems
Search engines
sequence modeling
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Summary: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.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2004.59