General Item Representation Learning for Cold-start Content Recommendations
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911848122548224 |
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| author | Kim, Jooeun Kim, Jinri Yeo, Kwangeun Kim, Eungi On, Kyoung-Woon Mun, Jonghwan Lee, Joonseok |
| author_facet | Kim, Jooeun Kim, Jinri Yeo, Kwangeun Kim, Eungi On, Kyoung-Woon Mun, Jonghwan Lee, Joonseok |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | General Item Representation Learning for Cold-start Content Recommendations Kim, Jooeun Kim, Jinri Yeo, Kwangeun Kim, Eungi On, Kyoung-Woon Mun, Jonghwan Lee, Joonseok Information Retrieval Machine Learning Multimedia 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. |
| title | General Item Representation Learning for Cold-start Content Recommendations |
| topic | Information Retrieval Machine Learning Multimedia |
| url | https://arxiv.org/abs/2404.13808 |