7 citations · 14 across the 9 of their papers we have counts for
9 papers
Memento: Personalized RAG-Style Long-Retention Data Scaling for META Ads Recommendation
Xiaoyu Chen, Ruichen Wang, Jieming Di +21
Modeling of long history data suffers from long-context window attention dilution, system efficiency and catastrophic forgetting problems, where naive linear scaling approach like…
LLMs + Persona-Plug = Personalized LLMs
Jiongnan Liu, Yutao Zhu, Shuting Wang +6
Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual inter…
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System
Xihong Yang, Heming Jing, Zixing Zhang +10
Benefiting from the strong reasoning capabilities, Large language models (LLMs) have demonstrated remarkable performance in recommender systems. Various efforts have been made to d…
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System
Xin Yang, Heng Chang, Zhijian Lai +6
Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and it has been g…
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets
Yu Lu, Junwei Bao, Zichen Ma +4
High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design. Existin…
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives
Zichen Ma, Yu Lu, Wenye Li +1
Personalized federated learning (FL) facilitates collaborations between multiple clients to learn personalized models without sharing private data. The mechanism mitigates the stat…
Increasing the Cost of Model Extraction with Calibrated Proof of Work
Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu +1
In model extraction attacks, adversaries can steal a machine learning model exposed via a public API by repeatedly querying it and adjusting their own model based on obtained predi…
Towards Heterogeneous Clients with Elastic Federated Learning
Zichen Ma, Yu Lu, Zihan Lu +3
Federated learning involves training machine learning models over devices or data silos, such as edge processors or data warehouses, while keeping the data local. Training in heter…
RevCore: Review-augmented Conversational Recommendation
Yu Lu, Junwei Bao, Yan Song +4
Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items. Incorporating e…