IS Journal 2004 Journal Article
Collaborative Filtering with Maximum Entropy
- D. Pavlov
- E. Manavoglu
- D.M. Pennock
- C. Lee Giles
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.