Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Short communication: Recommendation based on rational inferences in collaborative filtering
Knowledge-Based Systems
Fast gradient-descent methods for temporal-difference learning with linear function approximation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Adapting bias by gradient descent: an incremental version of delta-bar-delta
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Empirical Analysis of the Impact of Recommender Systems on Sales
Journal of Management Information Systems
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
A careful assessment of recommendation algorithms related to dimension reduction techniques
Knowledge-Based Systems
Knowledge-Based Systems
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Matrix Factorization (MF) based Collaborative Filtering (CF) have proved to be a highly accurate and scalable approach to recommender systems. In MF based CF, the learning rate is a key factor affecting the recommendation accuracy and convergence rate; however, this essential parameter is difficult to decide, since the recommender has to keep the balance between the recommendation accuracy and convergence rate. In this work, we choose the Regularized Matrix Factorization (RMF) based CF as the base model to discuss the effect of the learning rate in MF based CF, trying to deal with the dilemma of learning rate tuning through learning rate adaptation. First of all, we empirically validate the affection caused by the change of the learning rate on the recommendation performance. Subsequently, we integrate three sophisticated learning rate adapting strategies into RMF, including the Deterministic Step Size Adaption (DSSA), the Incremental Delta Bar Delta (IDBD), and the Stochastic Meta Decent (SMD). Thereafter, by analyzing the characteristics of the parameter update in RMF, we further propose the Gradient Cosine Adaption (GCA). The experimental results on five public large datasets demonstrate that by employing GCA, RMF could maintain good balance between accuracy and convergence rate, especially with small learning rate values.