General Item Representation Learning for Cold-start Content Recommendations

Autor:	Kim, Jooeun, Kim, Jinri, Yeo, Kwangeun, Kim, Eungi, On, Kyoung-Woon, Mun, Jonghwan, Lee, Joonseok
Rok vydání:	2024
Předmět:	Computer Science - Information Retrieval Computer Science - Machine Learning Computer Science - Multimedia
Druh dokumentu:	Working Paper
Popis:	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. Comment: 14 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.13808 Zobrazit plný text záznamu View this record from Arxiv