1 code implementation • 15 Mar 2024 • Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.
1 code implementation • 27 Nov 2023 • Xinrui He, Tianxin Wei, Jingrui He
Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w. r. t augmentations through within-behavior contrastive learning.
2 code implementations • 15 Nov 2023 • Hansi Zeng, Chen Luo, Bowen Jin, Sheikh Muhammad Sarwar, Tianxin Wei, Hamed Zamani
This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks.
1 code implementation • NeurIPS 2023 • Wenxuan Bao, Tianxin Wei, Haohan Wang, Jingrui He
To tackle this challenge, we propose a novel algorithm called ATP to adaptively learns the adaptation rates for each module in the model from distribution shifts among source domains.
1 code implementation • 11 Oct 2023 • Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen, Tianxin Wei, Ruirui Li, Zhengyang Wang, Zheng Li, Yang Li, Hanqing Lu, Suhang Wang, Jiawei Han, Xianfeng Tang
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
1 code implementation • 18 Jul 2023 • Tianxin Wei, Zeming Guo, Yifan Chen, Jingrui He
Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications.
1 code implementation • 7 Oct 2022 • Tianxin Wei, Yuning You, Tianlong Chen, Yang shen, Jingrui He, Zhangyang Wang
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL).
1 code implementation • 9 Jun 2022 • Tianxin Wei, Jingrui He
The core idea is to learn global shared meta-initialization parameters for all users and rapidly adapt them into local parameters for each user respectively.
no code implementations • 31 Jan 2022 • Yikun Ban, Yunzhe Qi, Tianxin Wei, Jingrui He
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation.
1 code implementation • 13 May 2021 • Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang
This work studies an unexplored problem in recommendation -- how to leverage popularity bias to improve the recommendation accuracy.
1 code implementation • 29 Oct 2020 • Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, JinFeng Yi, Xiangnan He
Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items.