Improving pairwise learning for item recommendation from implicit feedback

WSDM, 2014.

Cited by: 221|Bibtex|Views192|DOI:https://doi.org/10.1145/2556195.2556248
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Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

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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