XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System

Understand

Deep learning-based sequential recommender systems have recently attracted increasing attention from both academia and industry.

• Most of industrial Embedding-Based Retrieval (EBR) system for recommendation share the similar ideas with sequential recommenders.
• Among them, how to comprehensively capture sequential user interest is a fundamental problem.
• However, most existing sequential recommendation models take as input clicked or purchased behavior sequences from user-item interactions.