Abstract:AutoEncoder is an unsupervised learning approach that can maps inputs to useful intermediate features, which can be used to build recommendation. Intermediate features of different entities obtained by AutoEncoder may have different weight for predicting users behavior. However, existing research typically uses a uniform weight on intermediate features to make a fast learning algorithm, this general approach may lead to the limited performance of the model. In this paper, we proposes a novel approach by using … Show more
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