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Improving pairwise learning for item recommendation from implicit feedback

WSDM, pp.273-282, (2014)

Cited by: 276|Views345
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Abstract

Pairwise algorithms are popular for learning recommender systems from implicit feedback. For each user, or more generally context, they try to discriminate between a small set of selected items and the large set of remaining (irrelevant) items. Learning is typically based on stochastic gradient descent (SGD) with uniformly drawn pairs. In...More

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