8 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…
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…
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…
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…
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…
CHORD: Customizing Hybrid-precision On-device Model for Sequential Recommendation with Device-cloud Collaboration
Tianqi Liu, Kairui Fu, Shengyu Zhang +5
With the advancement of mobile device capabilities, deploying reranking models directly on devices has become feasible, enabling real-time contextual recommendations. When migratin…