5 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation
Wenzhuo Cheng, Ke Ding, Xin Dong +3
Conversion rate (CVR) prediction models play a vital role in recommendation and advertising systems. Recent research on multi-scenario recommendation shows that learning a unified…
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…
The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck Perspective
Binbin Hu, Weifan Wang, Hanshu Wang +4
Cross-domain Recommendation (CDR) aims to alleviate the data sparsity and the cold-start problems in traditional recommender systems by leveraging knowledge from an informative sou…
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors
Binzong Geng, Zhaoxin Huan, Xiaolu Zhang +5
With the rise of large language models (LLMs), recent works have leveraged LLMs to improve the performance of click-through rate (CTR) prediction. However, we argue that a critical…
AntMC: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li +10
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily s…