Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
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Ithaca: Cornell University Library, arXiv.org
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Language
English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user feedback for such target behavior prediction purposes is still an open question. Existing studies that...
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Full title
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
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TN_cdi_proquest_journals_2811756026
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2811756026
Other Identifiers
E-ISSN
2331-8422
DOI
10.48550/arxiv.2305.05585
