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WWW '03 Proceedings of the 12th international conference on World Wide Web
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CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A random walk method for alleviating the sparsity problem in collaborative filtering
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Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Collaborative filtering with temporal dynamics
Communications of the ACM
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Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Adapting neighborhood and matrix factorization models for context aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Factorization models for context-/time-aware movie recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
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Proceedings of the Workshop on Context-Aware Movie Recommendation
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Proceedings of the Workshop on Context-Aware Movie Recommendation
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Proceedings of the 16th ACM international conference on Supporting group work
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
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Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Time feature selection for identifying active household members
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This paper studies the rater identification problem in recommender systems. We propose to approach rater identification by fusing influence from various factors that relate to users. The rater likelihood is modeled from a probabilistic point of view as the conditional probability of a user given sources of contextual information, resulting in an aggregation model that fuses all the available information sources pertaining to a particular user. The result is a relational context-aware graph. A random walk with restart is used to calculate the proximity scores over this graph, which are used to identify raters. We compare our approach with several baselines in a set of experiments performed on the CAMRa2011 challenge dataset. The results demonstrate the superiority of our approach in predicting the identity of a rater who rated a particular movie within a given household.