1 code implementation • 20 Mar 2024 • Bowen Zheng, Zihan Lin, Enze Liu, Chen Yang, Enyang Bai, Cheng Ling, Wayne Xin Zhao, Ji-Rong Wen
Meanwhile, we leverage the LLM recommender as a supplemental component (discarded in deployment) to better capture underlying user preferences from heterogeneous interaction behaviors.
no code implementations • 18 Jan 2024 • Zihan Lin, Francisco Cruz, Eduardo Benitez Sandoval
In the multimodal, accuracy has experienced an elevation from 77. 48% to 78. 93%.
no code implementations • 3 Nov 2023 • Bo Xiong, Changqing Su, Zihan Lin, You Zhou, Zhaofei Yu
Here, we propose a neural rendering method for CT reconstruction, named Iterative Neural Adaptive Tomography (INeAT), which incorporates iterative posture optimization to effectively counteract the influence of posture perturbations in data, particularly in cases involving significant posture variations.
no code implementations • 14 Oct 2023 • Changqing Su, Zihan Lin, You Zhou, Shuai Wang, Yuhan Gao, Chenggang Yan, Bo Xiong
Moreover, by introducing the temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.
1 code implementation • 15 May 2023 • Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, Wayne Xin Zhao
Recently, large language models (LLMs) (e. g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.
no code implementations • ICCV 2023 • Yixin Zhang, Zilei Wang, Junjie Li, Jiafan Zhuang, Zihan Lin
We further propose a target-dominated cross-domain mixup that can incorporate accurate semantic information from the source domain.
no code implementations • ICCV 2023 • Zihan Lin, Zilei Wang, Yixin Zhang
In this study, we focus on Continual Semantic Segmentation (CSS) and present a novel approach to tackle the issue of existing methods struggling to learn new classes.
no code implementations • 29 Aug 2022 • Zihan Lin, Xuanhua Yang, Xiaoyu Peng, Wayne Xin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng
For this purpose, we build a relatedness prediction network, so that it can predict the contrast strength for inter-task representations of an instance.
2 code implementations • 15 Jun 2022 • Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, Ji-Rong Wen
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 and architectures.
no code implementations • 10 Jun 2022 • Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, Ji-Rong Wen
Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e. g., click, like and purchase).
1 code implementation • 13 Feb 2022 • Zihan Lin, Changxin Tian, Yupeng Hou, Wayne Xin Zhao
For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs.
1 code implementation • 3 Nov 2020 • Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, Ji-Rong Wen
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.