197 citations · 387 across the 12 of their papers we have counts for
12 papers
NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative Recommendation (GR), powered by Large Language Models (LLMs), represents a promising new paradigm for industrial recommender systems. However, their practical applicatio…
Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
Langming Liu, Wanyu Wang, Chi Zhang +6
Online advertising in recommendation platforms has gained significant attention, with a predominant focus on channel recommendation and budget allocation strategies. However, curre…
ECKGBench: Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graph
Langming Liu, Haibin Chen, Yuhao Wang +5
Large language models (LLMs) have demonstrated their capabilities across various NLP tasks. Their potential in e-commerce is also substantial, evidenced by practical implementation…
Efficient and Robust Regularized Federated Recommendation
Langming Liu, Wanyu Wang, Xiangyu Zhao +9
Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predo…
LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems
Langming Liu, Xiangyu Zhao, Chi Zhang +7
Transformer models have achieved remarkable success in sequential recommender systems (SRSs). However, computing the attention matrix in traditional dot-product attention mechanism…
Towards Robust Knowledge Tracing Models via k-Sparse Attention
Shuyan Huang, Zitao Liu, Xiangyu Zhao +2
Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interaction sequences. With the advanced capability of capturing contextu…
NoteLLM-2: Multimodal Large Representation Models for Recommendation
Chao Zhang, Haoxin Zhang, Shiwei Wu +6
Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks. However, their potential in multimodal representation, particularl…
AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation
Ziru Liu, Kecheng Chen, Fengyi Song +4
In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, th…
User Retention-oriented Recommendation with Decision Transformer
Kesen Zhao, Lixin Zou, Xiangyu Zhao +2
Improving user retention with reinforcement learning~(RL) has attracted increasing attention due to its significant importance in boosting user engagement. However, training the RL…
Multi-Task Recommendations with Reinforcement Learning
Ziru Liu, Jiejie Tian, Qingpeng Cai +8
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard t…
Denoising and Prompt-Tuning for Multi-Behavior Recommendation
Chi Zhang, Rui Chen, Xiangyu Zhao +2
In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering tec…
Towards Long-term Fairness in Recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao +8
As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior appro…