Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
Functional matrix factorizations for cold-start recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
ACM Transactions on Interactive Intelligent Systems (TiiS)
User effort vs. accuracy in rating-based elicitation
Proceedings of the sixth ACM conference on Recommender systems
Learning multiple-question decision trees for cold-start recommendation
Proceedings of the sixth ACM international conference on Web search and data mining
Modeling the uniqueness of the user preferences for recommendation systems
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Rating Bias and Preference Acquisition
ACM Transactions on Interactive Intelligent Systems (TiiS)
Interactive collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Interview process learning for top-n recommendation
Proceedings of the 7th ACM conference on Recommender systems
Semi-supervised discriminative preference elicitation for cold-start recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may be the quickest to abandon the system when disappointed. Rapid profiling of new users is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. This work offers a new bootstrapping method, which is based on a concrete optimization goal, thereby handily outperforming known approaches in our tests.