General Item Representation Learning for Cold-start Content Recommendations
<|reference_start|>General Item Representation Learning for Cold-start Content Recommendations: Cold-start item recommendation is a long-standing challenge in recommendation systems. A common remedy is to use a content-based approach, but rich information from raw contents in various forms has not been fully utilized. In this paper, we propose a domain/data-agnostic item representation learning framework for cold-start recommendations, naturally equipped with multimodal alignment among various features by adopting a Transformer-based architecture. Our proposed model is end-to-end trainable completely free from classification labels, not just costly to collect but suboptimal for recommendation-purpose representation learning. From extensive experiments on real-world movie and news recommendation benchmarks, we verify that our approach better preserves fine-grained user taste than state-of-the-art baselines, universally applicable to multiple domains at large scale.<|reference_end|>
@article{kim2024general,
    title={General Item Representation Learning for Cold-start Content
  Recommendations},
    author={Jooeun Kim, Jinri Kim, Kwangeun Yeo, Eungi Kim, Kyoung-Woon On,
  Jonghwan Mun, Joonseok Lee},
    journal={arXiv preprint arXiv:2404.13808},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13808},
    primaryClass={cs.IR cs.LG cs.MM}
}