General Item Representation Learning for Cold-start Content Recommendations

arXiv:2404.13808v1 Announce Type: cross
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 …

abstract arxiv challenge contents cs.ir cs.lg cs.mm data domain forms framework general information paper raw recommendation recommendations recommendation systems representation representation learning systems type