A Contextual Multi-armed Bandit Approach Based on Implicit Feedback for Online Recommendation

Yongquan Wan; Junli Xian; Cairong Yan

doi:10.1007/978-3-030-81635-3_31

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A Contextual Multi-armed Bandit Approach Based on Implicit Feedback for Online Recommendation

Yongquan Wan Junli Xian Cairong Yan

Year: 2021 Communications in computer and information science Pages: 380-392 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-81635-3_31

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Keywords:

Computer science Thompson sampling Leverage (statistics) Recommender system Popularity Context (archaeology) Multi-armed bandit Machine learning Artificial intelligence Information retrieval Data mining Regret

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2.13

FWCI (Field Weighted Citation Impact)

Refs

0.89

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Is in top 10%

Citation History

Topics

Advanced Bandit Algorithms Research

Social Sciences → Decision Sciences → Management Science and Operations Research

Recommender Systems and Techniques

Physical Sciences → Computer Science → Information Systems

Data Stream Mining Techniques

Physical Sciences → Computer Science → Artificial Intelligence

A Contextual Multi-armed Bandit Approach Based on Implicit Feedback for Online Recommendation

Metrics

Citation History

Topics

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