Multi-modal recommender systems: Hands-on exploration

Recommender systems typically learn from user-item preference data such as ratings and clicks. This information is sparse in nature, i.e., observed user-item preferences often represent less than 5% of possible interactions. One promising direction to alleviate data sparsity is to leverage auxiliary...

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Bibliographic Details
Main Authors: TRUONG, Quoc Tuan, SALAH, Aghiles, LAUW, Hady Wirawan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/6638
https://ink.library.smu.edu.sg/context/sis_research/article/7641/viewcontent/recsys21_tut.pdf
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Institution: Singapore Management University
Language: English
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