Multi-task learning for learning to rank in web search

41 citations recorded by the source corpus.

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An Overview of Multi-task Learning (2018, neutral): “Web applications based on MTL include learning to rank in web searches [121], web search ranking [122], multi-domain collaborative filtering [123], behavioral targeting [124], and conversion maximization in display advertising [125].”
A Survey on Multi-Task Learning (2017, methodology): “In [202], a multi-task boosting method, where different tasks share a feature representation, is proposed for learning to rank in web search.”
Learning to Rank for Information Retrieval and Natural Language Processing (2011, unknown): “Methods for domain adaptation, transfer learning, and multi-task learning have been proposed [9, 17, 19, 40].”
Feature-based transfer learning with real-world applications (2010, background): “Web applications of transfer learning include text classification across domains [151, 47, 48, 72, 195, 207, 36, 204, 213, 203], advertising [40, 39], learn to rank across domains [10, 192, 35, 68] and multi-domain collaborative filtering [103, 102, 29, 223, 143].”