Contrastive Learning for Cold-Start Recommendation

@article{Wei2021ContrastiveLF,
  title={Contrastive Learning for Cold-Start Recommendation},
  author={Yin-wei Wei and Xiang Wang and Qi Li and Liqiang Nie and Yan Li and Xuanping Li and Tat-Seng Chua},
  journal={Proceedings of the 29th ACM International Conference on Multimedia},
  year={2021}
}
Recommending purely cold-start items is a long-standing and fundamental challenge in the recommender systems. Without any historical interaction on cold-start items, the collaborative filtering (CF) scheme fails to leverage collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information of items (e.g. content features) into the CF scheme. Specifically, they employ modern neural network techniques (e.gā€¦Ā 

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