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Singh et al., 2020 - Google Patents

Valid explanations for learning to rank models

Singh et al., 2020

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Document ID
18050877210984720331
Author
Singh J
Wang Z
Khosla M
Anand A
Publication year
Publication venue
arXiv preprint arXiv:2004.13972

External Links

Snippet

Learning-to-rank (LTR) is a class of supervised learning techniques that apply to ranking problems dealing with a large number of features. The popularity and widespread application of LTR models in prioritizing information in a variety of domains makes their …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

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    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G06F17/30424—Query processing
    • G06F17/30533—Other types of queries
    • G—PHYSICS
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    • G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
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