SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Matrix factorization and neighbor based algorithms for the netflix prize problem
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A spatio-temporal approach to collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering via euclidean embedding
Proceedings of the fourth ACM conference on Recommender systems
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Recommender systems are considered as a promising approach to solve the problem of information overload. In collaborative filtering recommender systems, one of the most accurate and scalable algorithms is matrix factorization. As an alternative to this popular latent factor model, Euclidean embedding model presents the relationship between users and items intuitively, and generates recommendations fast. In this paper, a temporal Euclidean embedding (TEE) model is proposed by incorporating temporal factors of rating behavior. Through experiments on Netflix and Movielens data sets, we show the improvement of prediction accuracy, while keeping the efficiency of recommendation generation.