General Item Representation Learning for Cold-start Content Recommendations

• URL: http://arxiv.org/abs/2404.13808v1
• Date: Mon, 22 Apr 2024 00:48:56 GMT
• Title: General Item Representation Learning for Cold-start Content Recommendations
• Authors: Jooeun Kim, Jinri Kim, Kwangeun Yeo, Eungi Kim, Kyoung-Woon On, Jonghwan Mun, Joonseok Lee,
• Abstract summary: We propose a domain/data-agnostic item representation learning framework for cold-start recommendations. Our proposed model is end-to-end trainable completely free from classification labels.
• Score: 12.729624639270405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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