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- W2912603266 abstract "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." @default.
- W2912603266 created "2019-02-21" @default.
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- W2912603266 date "2019-01-22" @default.
- W2912603266 modified "2023-10-16" @default.
- W2912603266 title "Parallel pairwise learning to rank for collaborative filtering" @default.
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- W2912603266 doi "https://doi.org/10.1002/cpe.5141" @default.
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