  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.
