1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
Accelerating Recommender Model ETL with a Streaming FPGA-GPU Dataflow
Yu Zhu, Wenqi Jiang, Piyumi Jasin Pathiranage +2
The real-time performance of recommender models depends on the continuous integration of massive volumes of new user interaction data into training pipelines. While GPUs have scale…
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…