7 citations · 13 across the 2 of their papers we have counts for
3 papers
cs.IR2026
Climber-Pilot: A Non-Myopic Generative Recommendation Model Towards Better Instruction-Following
Da Guo, Shijia Wang, Qiang Xiao +7
Generative retrieval has emerged as a promising paradigm in recommender systems, offering superior sequence modeling capabilities over traditional dual-tower architectures. However…
cs.IR2025★ 7 cited
Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation
Shijia Wang, Tianpei Ouyang, Qiang Xiao +5
In music recommendation systems, multimodal interest learning is pivotal, which allows the model to capture nuanced preferences, including textual elements such as lyrics and vario…
cs.IR2025★ 6 cited
Climber: Toward Efficient Scaling Laws for Large Recommendation Models
Songpei Xu, Shijia Wang, Da Guo +5
Transformer-based generative models have achieved remarkable success across domains with various scaling law manifestations. However, our extensive experiments reveal persistent ch…