6 papers
From a Static Multi-Level Small Semantic Codebook to a Dynamic Single-Level Large Semantic Codebook for Generative Recommendation
Tianlu Xie, Xin Ku, Mingjie Sun +8
Generative recommendation represents each item with a sequence of discrete Semantic IDs (SIDs) and predicts the sequence to retrieve the next item. Typical systems use multi-level…
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
Yinpeng Wu, Yitong Chen, Lixiang Wang +3
Device-side Large Language Models (LLMs) have grown explosively, offering stronger privacy and higher availability than their cloud-side counterparts. During LLM inference, both th…
CS3: Efficient Online Capability Synergy for Two-Tower Recommendation
Lixiang Wang, Shaoyun Shi, Peng Wang +2
To balance effectiveness and efficiency in recommender systems, multi-stage pipelines employ lightweight two-tower models for large-scale candidate retrieval. However, their isolat…
CS3: Efficient Online Capability Synergy for Two-Tower Recommendation
Lixiang Wang, Shaoyun Shi, Peng Wang +2
To balance effectiveness and efficiency in recommender systems, multi-stage pipelines commonly use lightweight two-tower models for large-scale candidate retrieval. However, the is…
Generative Recommendation for Large-Scale Advertising
Ben Xue, Dan Liu, Lixiang Wang +27
Generative recommendation has recently attracted widespread attention in industry due to its potential for scaling and stronger model capacity. However, deploying real-time generat…
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
Yinpeng Wu, Yitong Chen, Lixiang Wang +3
Device-side Large Language Models (LLMs) have witnessed explosive growth, offering higher privacy and availability compared to cloud-side LLMs. During LLM inference, both model wei…