GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Statistical methods for speech recognition
Statistical methods for speech recognition
Adaptive Web sites: automatically synthesizing Web pages
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
An MDP-based recommender system
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Personalized ranking for digital libraries based on log analysis
Proceedings of the 10th ACM workshop on Web information and data management
Using Skipping for Sequence-Based Collaborative Filtering
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A low-order markov model integrating long-distance histories for collaborative recommender systems
Proceedings of the 14th international conference on Intelligent user interfaces
Bridging memory-based collaborative filtering and text retrieval
Information Retrieval
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
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The authors describe a novel maximum-entropy (maxent) approach for generating online recommendations as a user navigates through a collection of documents. They show how to handle high-dimensional sparse data and represent it as a collection of ordered sequences of document requests. This representation and the maxent approach have several advantages: (1) you can naturally model long-term interactions and dependencies in the data sequences; (2) you can query the model quickly once it is learned, which makes the method applicable to high-volume Web servers; and (3) you obtain empirically high-quality recommendations. Although maxent learning is computationally infeasible if implemented in the straightforward way, the authors explored data clustering and several algorithmic techniques to make learning practical even in high dimensions. They present several methods for combining the predictions of maxent models learned in different clusters. They conducted offline tests using over six months' worth of data from ResearchIndex, a popular online repository of over 470,000 computer science documents. They show that their maxent algorithm is one of the most accurate recommenders, as compared to such techniques as correlation, a mixture of Markov models, a mixture of multinomial models, individual similarity-based recommenders currently available on ResearchIndex, and even various combinations of current ResearchIndex recommenders.