← Search

Chu-Chun Yu

2 accepted papers

2026

MM4Rec: Multi-Source and Multi-Scenario Recommender for Unified User Preference

AAAI 2026technical

As online ecosystems grow increasingly complex, personalized recommendation systems must integrate user preferences across heterogeneous content sources and interaction scenarios. However, conventional methods typically model each source and scenario in isolation, hindering their ability to capture

Cited by 0SourcePDFScholar
2024

Push4Rec: Temporal and Contextual Trend-Aware Transformer Push Notification Recommender

ICASSP 2024accepted

Push notifications efficiently deliver real-time messages, boosting user engagement and website traffic. However, users often passively receive notifications without active interaction in recommendation contexts. Consequently, for precise recommendations, Click-Through Rate (CTR) prediction for push…

Cited by 0SourceScholar