58 citations · 93 across the 11 of their papers we have counts for
10 papers · 1 filter
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…
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…