GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A Bayesian approach toward active learning for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
Personalized active learning for collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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
Towards Optimal Active Learning for Matrix Factorization in Recommender Systems
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Active collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Active learning and search on low-rank matrices
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. However, different from the classic active learning, users (the "oracle") are not always able to provide an answer for queries. The easiest way to solve this problem is to ask most popular items, i.e items which have received many ratings from training users. But it is static and presents the same items to all users regardless of the ratings they have provided so far. In this paper we propose a method that improves the most popular selection strategy using the characteristics of matrix factorization. It finds similar users to the new user in the latent space and then selects item which is most popular among the similar users. The experimental results show the proposed method outperforms the most popular method both in terms of error and the number of received ratings.