6 citations · 8 across the 8 of their papers we have counts for
7 papers · 1 filter
TSGR: Taobao Search Generative Retrieval
Tianyu Zhan, Gui Ling, Tong Xiong +9
Generative retrieval (GR) has demonstrated strong promise for industrial e-commerce search by training a single autoregressive model to directly generate the Semantic IDs (SIDs) of…
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…
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…