Learning from Incomplete Ratings using Nonlinear Multi-layer Semi-Nonnegative Matrix Factorization
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Recommender systems problems witness a growing interest for finding better learning algorithms for personalized information. Matrix factorization that estimates the user liking for an item by taking an inner product on the latent features of users and item have been widely studied owing to its better accuracy and scalability. However, it is possible that the mapping between the latent features learned from these and the original features contains rather complex nonlinear hierarchical information, that classical linear matrix factorization can not capture. In this paper, we aim to propose a novel multilayer non-linear approach to a variant of nonnegative matrix factorization (NMF) to learn such factors from the incomplete ratings matrix. Firstly, we construct a user-item matrix with explicit ratings, secondly we learn latent factors for representations of users and items from the designed nonlinear multi-layer approach. Further, the architecture is built with different nonlinearities using adaptive gradient optimizer to better learn the latent factors in this space. We show that by doing so, our model is able to learn low-dimensional representations that are better suited for recommender systems on several benchmark datasets.
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