3 papers
cs.IR2026
ConAlign: Conditional Alignment Framework for Balancing Biased and Unbiased Recommendation
Jingcheng Zhang, Yihan Wang, Qi Song +1
Industry recommender systems trained on observational data suffer from various biases that create filter bubbles, causing user interests to collapse into narrow categories and seve…
cs.IR2025
Unleashing the Potential of Two-Tower Models: Diffusion-Based Cross-Interaction for Large-Scale Matching
Yihan Wang, Fei Xiong, Zhexin Han +3
Two-tower models are widely adopted in the industrial-scale matching stage across a broad range of application domains, such as content recommendations, advertisement systems, and…
cs.IR2024
Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking Framework
Yunfei Yang, Zhenghao Qi, Honghuan Wu +6
Video recommender systems (RSs) have gained increasing attention in recent years. Existing mainstream RSs focus on optimizing the matching function between users and items. However…