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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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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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 |