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
Machine Learning
Computational Statistics & Data Analysis - Nonlinear methods and data mining
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
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Commodity recommendations of retail business based on decisiontree induction
Expert Systems with Applications: An International Journal
On bootstrapping recommender systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Functional matrix factorizations for cold-start recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Active learning for technology enhanced learning
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
A recommendation model for handling dynamics in user profile
ICDCIT'12 Proceedings of the 8th international conference on Distributed Computing and Internet Technology
ACM Transactions on Interactive Intelligent Systems (TiiS)
Recommendation challenges in web media settings
Proceedings of the sixth ACM conference on Recommender systems
Influential seed items recommendation
Proceedings of the sixth ACM conference on Recommender systems
Exploiting the characteristics of matrix factorization for active learning in recommender systems
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
Adapting to natural rating acquisition with combined active learning strategies
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Using profile expansion techniques to alleviate the new user problem
Information Processing and Management: an International Journal
In the Mood4: recommendation by examples
Proceedings of the 16th International Conference on Extending Database Technology
Opportunity model for e-commerce recommendation: right product; right time
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Is it time for a career switch?
Proceedings of the 22nd international conference on World Wide Web
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
A case-based solution to the cold-start problem in group recommenders
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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
Facing the cold start problem in recommender systems
Expert Systems with Applications: An International Journal
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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 quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system 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. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.