Experiments in automatic statistical thesaurus construction
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Using statistical testing in the evaluation of retrieval experiments
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Fab: content-based, collaborative recommendation
Communications of the ACM
Automatic feedback using past queries: social searching?
Proceedings of the 20th 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
Local Feedback in Full-Text Retrieval Systems
Journal of the ACM (JACM)
Inverted file search algorithms for collaborative filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Improving new user recommendations with rule-based induction on cold user data
Proceedings of the 2007 ACM conference on Recommender systems
Applying Cross-Level Association Rule Mining to Cold-Start Recommendations
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Statistical user model supported by R-Tree structure
Applied Intelligence
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Collaborative Filtering techniques have become very popular in the last years as an effective method to provide personalized recommendations. They generally obtain much better accuracy than other techniques such as content-based filtering, because they are based on the opinions of users with tastes or interests similar to the user they are recommending to. However, this is precisely the reason of one of its main limitations: the cold-start problem. That is, how to recommend new items, not yet rated, or how to offer good recommendations to users they have not information about. For example, because they have recently joined the system. In fact, the new user problem is particularly serious, because an unsatisfied user may stop using the system before it could even collect enough information to generate good recommendations. In this article we tackle this problem with a novel approach called ''profile expansion'', based on the query expansion techniques used in Information Retrieval. In particular, we propose and evaluate three kinds of techniques: item-global, item-local and user-local. The experiments we have performed show that both item-global and user-local offer outstanding improvements in precision, up to 100%. Moreover, the improvements are statistically significant and consistent among different movie recommendation datasets and several training conditions.