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Parallel pairwise learning to rank for collaborative filtering
Author(s) -
Yağcı A. Murat,
Aytekin Tevfik,
Gürgen Fikret S.
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5141
Subject(s) - pairwise comparison , collaborative filtering , rank (graph theory) , computer science , stochastic gradient descent , range (aeronautics) , machine learning , learning to rank , theoretical computer science , artificial intelligence , recommender system , mathematics , artificial neural network , ranking (information retrieval) , combinatorics , materials science , composite material
Summary Pairwise learning to rank is known to be suitable for a wide range of collaborative filtering applications. In this work, we show that its efficiency can be greatly improved with parallel stochastic gradient descent schemes. Accordingly, we first propose to extrapolate two such state‐of‐the‐art schemes to the pairwise learning to rank problem setting. We then show the versatility of these proposals by showing the applicability of several important extensions commonly desired in practice. Theoretical as well as extensive empirical analyses of our proposals show remarkable efficiency results for pairwise learning to rank in offline and stream learning settings.