Mitigating popularity bias in recommendation with unbalanced interactions: A gradient perspective

Mitigating popularity bias in recommendation with unbalanced interactions: A gradient perspective

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to use...

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Bibliographic Details
Main Authors: REN, Weijieying, WANG, Lei, LIU, Kunpeng, GUO, Ruocheng, LIM, Ee-peng, FU, Yanjie
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
Subjects:
Training
Calibration
Data mining
Task analysis
Recommender systems
Optimization
Testing
Databases and Information Systems
Data Storage Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/7510
https://ink.library.smu.edu.sg/context/sis_research/article/8513/viewcontent/2211.01154.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/7510
https://ink.library.smu.edu.sg/context/sis_research/article/8513/viewcontent/2211.01154.pdf

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