6 citations · 6 across the 5 of their papers we have counts for
5 papers
Device-Cloud Collaborative Correction for On-Device Recommendation
Tianyu Zhan, Shengyu Zhang, Zheqi Lv +4
With the rapid development of recommendation models and device computing power, device-based recommendation has become an important research area due to its better real-time perfor…
Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation
Zheqi Lv, Tianyu Zhan, Wenjie Wang +6
Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to c…
Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)
Tianyu Zhan, Zheqi Lv, Shengyu Zhang +1
This paper explores the application and effectiveness of Test-Time Training (TTT) layers in improving the performance of recommendation systems. We developed a model, TTT4Rec, util…
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation
Kairui Fu, Shengyu Zhang, Zheqi Lv +2
Due to the continuously improving capabilities of mobile edges, recommender systems start to deploy models on edges to alleviate network congestion caused by frequent mobile reques…
MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities
Kunxi Li, Tianyu Zhan, Kairui Fu +6
In this study, we focus on heterogeneous knowledge transfer across entirely different model architectures, tasks, and modalities. Existing knowledge transfer methods (e.g., backbon…