Multi-modal recommender systems: Hands-on exploration

• Main Authors: TRUONG, Quoc Tuan, SALAH, Aghiles, LAUW, Hady Wirawan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Recommender systems; Auxiliary information; Cross model; Data sparsity; Hands-on exercise; Learn+; Multi-modal; Preference data; Product images; Databases and Information Systems
• 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
• Institution: Singapore Management University

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...