Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Interpretable nonnegative matrix decompositions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tied boltzmann machines for cold start recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
AdHeat: an influence-based diffusion model for propagating hints to match ads
Proceedings of the 19th international conference on World wide web
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting product adoption in large-scale social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Adaptive sampling and fast low-rank matrix approximation
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
User effort vs. accuracy in rating-based elicitation
Proceedings of the sixth ACM conference on Recommender systems
Influential seed items recommendation
Proceedings of the sixth ACM conference on Recommender systems
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
Addressing cold-start in app recommendation: latent user models constructed from twitter followers
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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 have to deal with the cold start problem as new users and/or items are always present. Rating elicitation is a common approach for handling cold start. However, there still lacks a principled model for guiding how to select the most useful ratings. In this paper, we propose a principled approach to identify representative users and items using representative-based matrix factorization. Not only do we show that the selected representatives are superior to other competing methods in terms of achieving good balance between coverage and diversity, but we also demonstrate that ratings on the selected representatives are much more useful for making recommendations (about 10% better than competing methods). In addition to illustrating how representatives help solve the cold start problem, we also argue that the problem of finding representatives itself is an important problem that would deserve further investigations, for both its practical values and technical challenges.