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- W3027227670 abstract "Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general The setting we consider is one in which no analytic description of what constitutes a good ranking is available. Instead, we have a collection of representations and supervisory information consisting of a (target item, interesting items set) pair. We demonstrate -- analytically, in simulation, and in real data examples -- that learning to rank via combining representations using an integer linear program is effective when the supervision is as light as these few items are similar to your item of interest. While this nomination task is of general interest, for specificity we present our methodology from the perspective of vertex nomination in graphs. The methodology described herein is model agnostic." @default.
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- W3027227670 date "2020-05-20" @default.
- W3027227670 modified "2023-10-18" @default.
- W3027227670 title "Learning to rank via combining representations." @default.
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