42 citations · 88 across the 34 of their papers we have counts for
9 papers · 1 filter
Semantic Trimming and Auxiliary Multi-step Prediction for Generative Recommendation
Tianyu Zhan, Kairui Fu, Chengfei Lv +2
Generative Recommendation (GR) has recently transitioned from atomic item-indexing to Semantic ID (SID)-based frameworks to capture intrinsic item relationships and enhance general…
RASTP: Representation-Aware Semantic Token Pruning for Generative Recommendation with Semantic Identifiers
Tianyu Zhan, Kairui Fu, Zheqi Lv +1
Generative recommendation systems typically leverage Semantic Identifiers (SIDs), which represent each item as a sequence of tokens that encode semantic information. However, repre…
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…
ThinkRec: Thinking-based recommendation via LLM
Qihang Yu, Kairui Fu, Zheqi Lv +6
Recent advances in large language models (LLMs) have enabled more semantic-aware recommendations through natural language generation. Existing LLM for recommendation (LLM4Rec) meth…
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…
Semantic Codebook Learning for Dynamic Recommendation Models
Zheqi Lv, Shaoxuan He, Tianyu Zhan +5
Dynamic sequential recommendation (DSR) can generate model parameters based on user behavior to improve the personalization of sequential recommendation under various user preferen…
Intelligent Model Update Strategy for Sequential Recommendation
Zheqi Lv, Wenqiao Zhang, Zhengyu Chen +2
Modern online platforms are increasingly employing recommendation systems to address information overload and improve user engagement. There is an evolving paradigm in this researc…
Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling
Zheqi Lv, Feng Wang, Shengyu Zhang +3
In an era of information explosion, recommendation systems play an important role in people's daily life by facilitating content exploration. It is known that user activeness, i.e.…