Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Machine Learning
Machine Learning
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
A Bayesian approach toward active learning for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Decision-Centric Active Learning of Binary-Outcome Models
Information Systems Research
Active learning with statistical models
Journal of Artificial Intelligence Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Active collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Active Feature-Value Acquisition
Management Science
Who will be participating next?: predicting the participation of Dark Web community
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
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Accurate prediction of customer preferences on products is the key to any recommender systems to realize its promised strategic values such as improved customer satisfaction and therefore enhanced loyalty. In this paper, we propose proactively acquiring ratings from customers for a newly introduced product to quickly improve the accuracy of the predicted ratings generated by a collaborative filtering recommendation algorithm for the entire customer population. We formally introduce the problem of identifying the most informative ratings to acquire and termed it as the product rating acquisition problem. We proposed an active learning sampling method for this problem that is generic to any recommendation algorithms. Using the Netflix Prize dataset, we experimented with our proposed method, a uniform random sampling method, and a degree-based sampling method that is biased toward customers with large numbers of ratings for the user-based and item-based neighborhood recommendation algorithms. The experimental results showed that even with the random sampling method, acquiring 10% of all ratings in addition to a randomly selected 10% initial ratings achieved 4.5% improvement on overall rating prediction accuracy of the movie. In addition, our proposed active learning sampling method consistently outperformed the random and degree-based sampling for the better-performing item-based algorithm and achieved more than 8% improvement by acquiring 10% of the ratings.