Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
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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. The additional complication in using standard methods for matrix decompositions appears when the initial data are ratings, i.e. they are represented in the ordinal scale. Standard methods are used for quantitative data. In this paper a new incremental gradient method based on Generalized Hebbian Algorithm (GHA) is proposed. It allows to find matrix decompositions for ordinal data bulks. The functional for ordinal data is worked in. 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 the better RMSE when applying an algorithm adjusted to ordinal data.