1 citations · 1 across the 1 of their papers we have counts for
7 papers
FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets
Kairui Fu, Tao Zhang, Shuwen Xiao +9
Semantic identifiers (SIDs) have gained increasing attention in generative retrieval (GR) for recommendation due to their meaningful semantic discriminability. However, current stu…
RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation
Kairui Fu, Changfa Wu, Kun Yuan +8
Generative retrieval (GR) has emerged as a promising paradigm in recommendation systems by autoregressively decoding identifiers of target items. Despite its potential, current app…
MALLOC: Benchmarking the Memory-aware Long Sequence Compression for Large Sequential Recommendation
Qihang Yu, Kairui Fu, Zhaocheng Du +10
The scaling law, which indicates that model performance improves with increasing dataset and model capacity, has fueled a growing trend in expanding recommendation models in both i…
Tackling Device Data Distribution Real-time Shift via Prototype-based Parameter Editing
Zheqi Lv, Wenqiao Zhang, Kairui Fu +6
The on-device real-time data distribution shift on devices challenges the generalization of lightweight on-device models. This critical issue is often overlooked in current researc…
Optimize Incompatible Parameters through Compatibility-aware Knowledge Integration
Zheqi Lv, Keming Ye, Zishu Wei +7
Deep neural networks have become foundational to advancements in multiple domains, including recommendation systems, natural language processing, and so on. Despite their successes…
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…