UMFE: a user modelling front-end subsystem
International Journal of Man-Machine Studies
Modeling the user in natural language systems
Computational Linguistics - Special issue on user modeling
User models in dialog systems
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
ACM Computing Surveys (CSUR)
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
Information Retrieval
Machine Learning
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Intelligent Systems
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Interfaces for eliciting new user preferences in recommender systems
UM'03 Proceedings of the 9th international conference on User modeling
An economic model of user rating in an online recommender system
UM'05 Proceedings of the 10th international conference on User Modeling
On bootstrapping recommender systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Categorizing user interests in recommender systems
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
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
Proceedings of the fifth ACM conference on Recommender systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
User profiling vs. accuracy in recommender system user experience
Proceedings of the International Working Conference on Advanced Visual Interfaces
User effort vs. accuracy in rating-based elicitation
Proceedings of the sixth ACM conference on Recommender systems
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system
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
Designing an Information Gathering Application for a Personalized Travel Recommender System
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Lattice navigation for collaborative filtering by means of (fuzzy) formal concept analysis
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Knowledge-Based Systems
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
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
Defending recommender systems by influence analysis
Information Retrieval
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Recommender systems are an effective tool to help find items of interest from an overwhelming number of available items. Collaborative Filtering (CF), the best known technology for recommender systems, is based on the idea that a set of like-minded users can help each other find useful information. A new user poses a challenge to CF recommenders, since the system has no knowledge about the preferences of the new user, and therefore cannot provide personalized recommendations. A new user preference elicitation strategy needs to ensure that the user does not a) abandon a lengthy signup process, and b) lose interest in returning to the site due to the low quality of initial recommendations. We extend the work of [23] in this paper by incrementally developing a set of information theoretic strategies for the new user problem. We propose an offline simulation framework, and evaluate the strategies through extensive offline simulations and an online experiment with real users of a live recommender system.