Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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In modern collaborative filtering applications initial data are typically very large (holding millions of users and items) and come in real time. In this case only incremental algorithms are practically efficient. In this paper a new algorithm based on the symbiosis of Incremental Singular Value Decomposition (ISVD) and Generalized Hebbian Algorithm (GHA) is proposed. The algorithm does not require to store the initial data matrix and effectively updates user/item profiles when a new user or a new item appears or a matrix cell is modified. The results of experiments show how root mean square error (RMSE) depends on the number of algorithm's iterations and data amount.