1 citations · 1 across the 6 of their papers we have counts for
6 papers
PRIME: Mitigating Subgroup Optimization Competition in Shared CTR Top Networks with Plug-in Residual Input-Conditioned Mixture of Expert
Heng Yao, Siyun Hou, Tianying Liu +8
Click-through rate (CTR) models vary in feature-interaction design, yet their top networks usually remain a single multilayer perceptron shared by all examples. Heterogeneous user,…
SmartGR: Hierarchy and Beam-Aware Knowledge Distillation for Generative Recommendation
Ziheng Zhang, Yu Cui, Bohao Wang +6
Generative recommendation (GR) has emerged as a promising paradigm for recommender systems. Scaling up GR models can improve recommendation performance, but it also substantially i…
Trie-Aware Transformers for Generative Recommendation
Zhenxiang Xu, Jiawei Chen, Sirui Chen +5
Generative recommendation (GR) aligns with advances in generative AI by casting next-item prediction as token-level generation rather than score-based ranking. Most GR methods adop…
A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Chao Xiong, Xianwen Yu, Wei Xu +3
Pre-ranking plays a crucial role in large-scale recommender systems by significantly improving the efficiency and scalability within the constraints of providing high-quality candi…
CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction
Zixuan Li, Binzong Geng, Jing Xiong +11
Click-Through Rate (CTR) prediction, a core task in recommendation systems, estimates user click likelihood using historical behavioral data. Modeling user behavior sequences as te…
DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval
Penghao Lu, Xin Dong, Yuansheng Zhou +3
Generative retrieval constitutes an innovative approach in information retrieval, leveraging generative language models (LM) to generate a ranked list of document identifiers (doci…