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
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
| Hi-index | 0.00 |
Collaborative Filtering (CF) can be achieved by Matrix Factorization (MF) with high prediction accuracy and scalability. Most of the current MF based recommenders, however, are serial, which prevent them sharing the efficiency brought by the rapid progress in parallel programming techniques. Aiming at parallelizing the CF recommender based on Regularized Matrix Factorization (RMF), we first carry out the theoretical analysis on the parameter updating process of RMF, whereby we can figure out that the main obstacle preventing the model from parallelism is the inter-dependence between item and user features. To remove the inter-dependence among parameters, we apply the Alternating Stochastic Gradient Solver (ASGD) solver to deal with the parameter training process. On this basis, we subsequently propose the parallel RMF (P-RMF) model, of which the training process can be parallelized through simultaneously training different user/item features. Experiments on two large, real datasets illustrate that our P-RMF model can provide a faster solution to CF problem when compared to the original RMF and another parallel MF based recommender.