58 citations · 93 across the 11 of their papers we have counts for
12 papers
Collaborative Memory Augmentation for Generative Recommendation
Enze Liu, Zhen Tian, Wayne Xin Zhao
Generative Recommendation (GR) has exhibited great potential by modeling item transitions as a sequence-to-sequence task. Despite the success of GR, existing frameworks primarily f…
Dual-Stream MLP is All You Need for CTR Prediction
Kesha Ou, Zhen Tian, Wayne Xin Zhao +3
Click-through rate (CTR) prediction holds a pivotal role in online advertising and recommendation systems, where even small improvements can significantly boost revenue. Existing r…
GenCI: Generative Modeling of User Interest Shift via Cohort-based Intent Learning for CTR Prediction
Kesha Ou, Zhen Tian, Wayne Xin Zhao +2
Click-through rate (CTR) prediction plays a pivotal role in online advertising and recommender systems. Despite notable progress in modeling user preferences from historical behavi…
Irrational Complex Rotations Empower Low-bit Optimizers
Zhen Tian, Wayne Xin Zhao, Ji-Rong Wen
In this paper, we propose a novel optimizer state compression algorithm, namely -Quant, which leverages the properties of irrational numbers (e.g., ) for memory-efficient tra…
Exploring Context Window of Large Language Models via Decomposed Positional Vectors
Zican Dong, Junyi Li, Xin Men +5
Transformer-based large language models (LLMs) typically have a limited context window, resulting in significant performance degradation when processing text beyond the length of t…
EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention
Zhen Tian, Wayne Xin Zhao, Changwang Zhang +3
To capture user preference, transformer models have been widely applied to model sequential user behavior data. The core of transformer architecture lies in the self-attention mech…
Sequence-level Semantic Representation Fusion for Recommender Systems
Lanling Xu, Zhen Tian, Bingqian Li +4
With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the u…
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
Zhen Tian, Changwang Zhang, Wayne Xin Zhao +3
Click-Through Rate (CTR) prediction, which aims to estimate the probability of a user clicking on an item, is a key task in online advertising. Numerous existing CTR models concent…
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
Zhen Tian, Ting Bai, Wayne Xin Zhao +2
Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with…
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
Zhen Tian, Ting Bai, Zibin Zhang +4
With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely incr…
Recent Advances in RecBole: Extensions with more Practical Considerations
Lanling Xu, Zhen Tian, Gaowei Zhang +10
RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update request…
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan +16
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics a…