MARec: Metadata Alignment for cold-start Recommendation
<|reference_start|>MARec: Metadata Alignment for cold-start Recommendation: For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can be far larger than the number of clicks. Such sparsity is accentuated in cold-start settings, which makes the efficient use of metadata information of paramount importance. In this work, we propose a simple approach to address cold-start recommendations by leveraging content metadata, Metadata Alignment for cold-start Recommendation. We show that this approach can readily augment existing matrix factorization and autoencoder approaches, enabling a smooth transition to top performing algorithms in warmer set-ups. Our experimental results indicate three separate contributions: first, we show that our proposed framework largely beats SOTA results on 4 cold-start datasets with different sparsity and scale characteristics, with gains ranging from +8.4% to +53.8% on reported ranking metrics; second, we provide an ablation study on the utility of semantic features, and proves the additional gain obtained by leveraging such features ranges between +46.8% and +105.5%; and third, our approach is by construction highly competitive in warm set-ups, and we propose a closed-form solution outperformed by SOTA results by only 0.8% on average.<|reference_end|>
@article{monteil2024marec:,
    title={MARec: Metadata Alignment for cold-start Recommendation},
    author={Julien Monteil, Volodymyr Vaskovych, Wentao Lu, Anirban Majumder,
  Anton van den Hengel},
    journal={arXiv preprint arXiv:2404.13298},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13298},
    primaryClass={cs.IR cs.SY eess.SY}
}