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Tsang et al., 2020 - Google Patents

Feature interaction interpretability: A case for explaining ad-recommendation systems via neural interaction detection

Tsang et al., 2020

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Document ID
3857662297580644261
Author
Tsang M
Cheng D
Liu H
Feng X
Zhou E
Liu Y
Publication year
Publication venue
arXiv preprint arXiv:2006.10966

External Links

Snippet

Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

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    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • 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
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    • G06Q10/00—Administration; Management

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