General Item Representation Learning for Cold-start Content Recommendations

Understand

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.