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 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.

Authors

• D. Pavlov
• E. Manavoglu
• D.M. Pennock
• C. Lee Giles The Pennsylvania State University

Keywords

• Collaboration
• Filtering
• Entropy
• Navigation
• Computer science
• Context modeling
• Collaborative work
• Bayesian methods
• History
• Search engines
• Maximum Entropy
• Collaborative Filtering
• Word Of Mouth
• Online Shopping
• Recommender Systems
• User Access
• Relatively Compact
• Online Search Engines
• Training Data
• Test Data
• Body Height
• Probabilistic Model
• Mixture Model
• Expectation Maximization
• Multinomial Regression
• Multinomial Model
• Top Features
• User Sessions
• maximum entropy model
• sequence modeling
• mixture models