1 code implementation • 24 Apr 2024 • Zhiyu He, Jiayu Li, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma
Meanwhile, EEG signals are collected with a portable device.
no code implementations • 1 Apr 2024 • Shaorun Zhang, Zhiyu He, Ziyi Ye, Peijie Sun, Qingyao Ai, Min Zhang, Yiqun Liu
To address these challenges and provide a more comprehensive understanding of user affective experience and cognitive activity, we propose EEG-SVRec, the first EEG dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation.
no code implementations • 1 Apr 2024 • Haitao Li, You Chen, Zhekai Ge, Qingyao Ai, Yiqun Liu, Quan Zhou, Shuai Huo
Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system.
1 code implementation • 27 Mar 2024 • Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, Mingchen Cai
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
1 code implementation • 27 Mar 2024 • Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang
Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.
no code implementations • 27 Mar 2024 • Haitao Li, Qingyao Ai, Jia Chen, Qian Dong, Zhijing Wu, Yiqun Liu, Chong Chen, Qi Tian
However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc.
no code implementations • 27 Mar 2024 • Haitao Li, Qingyao Ai, Xinyan Han, Jia Chen, Qian Dong, Yiqun Liu, Chong Chen, Qi Tian
Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity.
no code implementations • 27 Mar 2024 • Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, Yiqun Liu
In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models.
1 code implementation • 27 Mar 2024 • Jingtao Zhan, Qingyao Ai, Yiqun Liu, Jia Chen, Shaoping Ma
Our in-depth analysis of these logs reveals that user prompt reformulation is heavily dependent on the individual user's capability, resulting in significant variance in the quality of reformulation pairs.
no code implementations • 20 Mar 2024 • Ruizhe Zhang, Qingyao Ai, Ziyi Ye, Yueyue Wu, Xiaohui Xie, Yiqun Liu
Traditional feedback signal such as clicks is too coarse to use as they do not reflect any fine-grained relevance information.
no code implementations • 17 Mar 2024 • Ruizhe Zhang, Haitao Li, Yueyue Wu, Qingyao Ai, Yiqun Liu, Min Zhang, Shaoping Ma
In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains.
1 code implementation • 15 Mar 2024 • Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, Yiqun Liu
Our framework is specifically designed to make decisions on when and what to retrieve based on the LLM's real-time information needs during the text generation process.
no code implementations • 11 Mar 2024 • Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, Yujia Zhou, Yiqun Liu
Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate.
no code implementations • 25 Feb 2024 • Ruizhe Zhang, Qingyao Ai, Yiqun Liu, Yueyue Wu, Beining Wang
Gender of the defendants in both the task and relevant cases was edited to statistically measure the effect of gender bias in the legal case search results on participants' perceptions.
1 code implementation • 24 Feb 2024 • Ziyi Ye, Jingtao Zhan, Qingyao Ai, Yiqun Liu, Maarten de Rijke, Christina Lioma, Tuukka Ruotsalo
If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well.
no code implementations • 28 Jan 2024 • Zhumin Chu, Qingyao Ai, Yiteng Tu, Haitao Li, Yiqun Liu
Existing paradigms rely on either human annotators or model-based evaluators to evaluate the performance of LLMs on different tasks.
no code implementations • 17 Dec 2023 • Yiqun Liu, Mohsen Abdoli, Thomas Guionnet, Christine Guillemot, Aline Roumy
Compared to the High Efficiency Video Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in terms of Bjontegaard Delta-Rate (BD-rate), at the cost of about 10x more encoder complexity.
1 code implementation • 17 Dec 2023 • Weihang Su, Qingyao Ai, Xiangsheng Li, Jia Chen, Yiqun Liu, Xiaolong Wu, Shengluan Hou
With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems.
1 code implementation • 9 Dec 2023 • Ziyi Ye, Xiaohui Xie, Qingyao Ai, Yiqun Liu, Zhihong Wang, Weihang Su, Min Zhang
To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking.
1 code implementation • 17 Nov 2023 • Shenghao Yang, Chenyang Wang, Yankai Liu, Kangping Xu, Weizhi Ma, Yiqun Liu, Min Zhang, Haitao Zeng, Junlan Feng, Chao Deng
In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation.
1 code implementation • 16 Nov 2023 • Ziyi Ye, Qingyao Ai, Yiqun Liu, Maarten de Rijke, Min Zhang, Christina Lioma, Tuukka Ruotsalo
Inspired by recent research that revealed associations between the brain and the large computational language models, we propose a generative language BCI that utilizes the capacity of a large language model (LLM) jointly with a semantic brain decoder to directly generate language from functional magnetic resonance imaging (fMRI) input.
1 code implementation • 1 Nov 2023 • Weihang Su, Qingyao Ai, Yueyue Wu, Yixiao Ma, Haitao Li, Yiqun Liu, Zhijing Wu, Min Zhang
Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments.
no code implementations • 26 Oct 2023 • Haitao Li, Yunqiu Shao, Yueyue Wu, Qingyao Ai, Yixiao Ma, Yiqun Liu
However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling.
2 code implementations • 20 Oct 2023 • Yiqun Liu, Marc Riviere, Thomas Guionnet, Aline Roumy, Christine Guillemot
Experiments show that the proposed method can achieve acceleration ranging from 16. 5% to 60. 2% under the RandomAccess Group Of Picture 32 (RAGOP32) configuration with a reasonable efficiency drop ranging from 0. 44% to 4. 59% in terms of BD-rate, which surpasses other state-of-the-art solutions.
no code implementations • 7 Oct 2023 • Beining Wang, Ruizhe Zhang, Yueyue Wu, Qingyao Ai, Min Zhang, Yiqun Liu
Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand.
no code implementations • 29 Sep 2023 • Qian Dong, Yiding Liu, Qingyao Ai, Zhijing Wu, Haitao Li, Yiqun Liu, Shuaiqiang Wang, Dawei Yin, Shaoping Ma
Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR).
1 code implementation • 27 Sep 2023 • Kaiyuan Zhang, Ziyi Ye, Qingyao Ai, Xiaohui Xie, Yiqun Liu
Recognizing this shortfall, there has been a burgeoning interest in recent years in harnessing the potential of Graph Neural Networks (GNN) to exploit the topological information by modeling features selected from each EEG channel in a graph structure.
no code implementations • 25 Jul 2023 • Yunqiu Shao, Haitao Li, Yueyue Wu, Yiqun Liu, Qingyao Ai, Jiaxin Mao, Yixiao Ma, Shaoping Ma
Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval.
no code implementations • 19 Jul 2023 • Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
no code implementations • 1 Jul 2023 • Weihang Su, Xiangsheng Li, Yiqun Liu, Min Zhang, Shaoping Ma
Our team(THUIR2) participated in both FOSS and POSS subtasks of the NTCIR-161 Session Search (SS) Task.
1 code implementation • 4 Jun 2023 • Qian Dong, Yiding Liu, Qingyao Ai, Haitao Li, Shuaiqiang Wang, Yiqun Liu, Dawei Yin, Shaoping Ma
Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance.
2 code implementations • 11 May 2023 • Haitao Li, Changyue Wang, Weihang Su, Yueyue Wu, Qingyao Ai, Yiqun Liu
This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task.
2 code implementations • 11 May 2023 • Haitao Li, Weihang Su, Changyue Wang, Yueyue Wu, Qingyao Ai, Yiqun Liu
Legal case retrieval techniques play an essential role in modern intelligent legal systems.
no code implementations • 9 May 2023 • Yixiao Ma, Yueyue Wu, Weihang Su, Qingyao Ai, Yiqun Liu
In the data sampling phase, we enhance the quality of the training data by utilizing fine-grained law article information to guide the selection of positive and negative examples.
1 code implementation • 28 Apr 2023 • Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yiqun Liu, Yixing Fan, Xueqi Cheng
Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task.
1 code implementation • 25 Apr 2023 • Jia Chen, Haitao Li, Weihang Su, Qingyao Ai, Yiqun Liu
This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank.
1 code implementation • 24 Apr 2023 • Haitao Li, Qingyao Ai, Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Zheng Liu, Zhao Cao
Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance.
1 code implementation • 22 Apr 2023 • Haitao Li, Qingyao Ai, Jia Chen, Qian Dong, Yueyue Wu, Yiqun Liu, Chong Chen, Qi Tian
Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements.
1 code implementation • 7 Apr 2023 • Xiaohui Xie, Qian Dong, Bingning Wang, Feiyang Lv, Ting Yao, Weinan Gan, Zhijing Wu, Xiangsheng Li, Haitao Li, Yiqun Liu, Jin Ma
T2Ranking comprises more than 300K queries and over 2M unique passages from real-world search engines.
no code implementations • 28 Feb 2023 • Haitao Li, Jia Chen, Weihang Su, Qingyao Ai, Yiqun Liu
This paper describes the approach of the THUIR team at the WSDM Cup 2023 Pre-training for Web Search task.
no code implementations • 29 Jan 2023 • Ruizhe Zhang, Qingyao Ai, Yueyue Wu, Yixiao Ma, Yiqun Liu
In the process of searching, legal practitioners often need the search results under several different causes of cases as reference.
no code implementations • 19 Oct 2022 • Tetsuya Sakai, Sijie Tao, Maria Maistro, Zhumin Chu, Yujing Li, Nuo Chen, Nicola Ferro, Junjie Wang, Ian Soboroff, Yiqun Liu
The noise is due to a fatal bug in the backend of our relevance assessment interface.
1 code implementation • 17 Aug 2022 • Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuesong Chen, Min Zhang, Shaoping Ma
We explore the effectiveness of BTA for satisfaction modeling in two popular information access scenarios, i. e., search and recommendation.
1 code implementation • 16 Aug 2022 • Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yiqun Liu, Yixing Fan, Xueqi Cheng
We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning.
1 code implementation • 11 Aug 2022 • Jingtao Zhan, Qingyao Ai, Yiqun Liu, Jiaxin Mao, Xiaohui Xie, Min Zhang, Shaoping Ma
By making the REM and DAMs disentangled, DDR enables a flexible training paradigm in which REM is trained with supervision once and DAMs are trained with unsupervised data.
1 code implementation • 12 Jul 2022 • Guoxia Wang, Xiaomin Fang, Zhihua Wu, Yiqun Liu, Yang Xue, Yingfei Xiang, dianhai yu, Fan Wang, Yanjun Ma
Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to implement the training and inference of AlphaFold2 from scratch.
2 code implementations • 26 Jun 2022 • Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, Shaoping Ma
Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance.
no code implementations • 8 Jun 2022 • Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma
First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues.
1 code implementation • 25 Apr 2022 • Jingtao Zhan, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation.
1 code implementation • 6 Apr 2022 • Zhumin Chu, Qingyao Ai, Zhihong Wang, Yiqun Liu, Yingye Huang, Rui Zhang, Min Zhang, Shaoping Ma
This raises the question of how to accurately model user satisfaction in conversational search scenarios.
no code implementations • 5 Apr 2022 • Yangkun Li, Weizhi Ma, Chong Chen, Min Zhang, Yiqun Liu, Shaoping Ma, Yuekui Yang
Among various methods of coping with overfitting, dropout is one of the representative ways.
no code implementations • 8 Feb 2022 • Kaushik Rangadurai, Yiqun Liu, Siddarth Malreddy, Xiaoyi Liu, Piyush Maheshwari, Vishwanath Sangale, Fedor Borisyuk
In this paper, we present NxtPost, a deployed user-to-post content-based sequential recommender system for Facebook Groups.
no code implementations • 27 Nov 2021 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success.
no code implementations • 14 Oct 2021 • Xuesong Chen, Ziyi Ye, Xiaohui Xie, Yiqun Liu, Weihang Su, Shuqi Zhu, Min Zhang, Shaoping Ma
While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades.
4 code implementations • 12 Oct 2021 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space.
no code implementations • 22 Sep 2021 • Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuancheng Li, Jiaji Li, Xuesong Chen, Min Zhang, Shaoping Ma
Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i. e., content and context factors).
1 code implementation • 3 Aug 2021 • Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuesong Chen, Min Zhang, Shaoping Ma
In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension.
5 code implementations • 2 Aug 2021 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size.
2 code implementations • 11 Jun 2021 • Bin Hao, Min Zhang, Weizhi Ma, Shaoyun Shi, Xinxing Yu, Houzhi Shan, Yiqun Liu, Shaoping Ma
To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.
4 code implementations • 16 Apr 2021 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance.
1 code implementation • 27 Mar 2021 • Yiqun Liu, Yi Zeng, Jian Pu, Hongming Shan, Peiyang He, Junping Zhang
In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones.
no code implementations • 24 Dec 2020 • Yunqiu Shao, Bulou Liu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
We participated in the two case law tasks, i. e., the legal case retrieval task and the legal case entailment task.
2 code implementations • 20 Oct 2020 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
Through this process, it teaches the DR model how to retrieve relevant documents from the entire corpus instead of how to rerank a potentially biased sample of documents.
1 code implementation • ECCV 2020 • Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, Jue Wang
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.
no code implementations • 1 Aug 2020 • Jie Zou, Evangelos Kanoulas, Yiqun Liu
Search and recommender systems that take the initiative to ask clarifying questions to better understand users' information needs are receiving increasing attention from the research community.
2 code implementations • 1 Jul 2020 • Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma
However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.
3 code implementations • 28 Jun 2020 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings.
2 code implementations • WWW 2020 • Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma Department of Computer Science and Technology, Institute for Articial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University cc17@mails.tsinghua.edu.cn, z-m@tsinghua.edu.cn
Factorization Machines (FM) with negative sampling is a popular solution for context-aware recommendation.
no code implementations • 13 Apr 2020 • Yiqun Liu, Shouzhen Chen, Lei Chen, Hai Chu, Xiaoyang Xu, Junping Zhang, Leiming Ma
We thus propose an end-to-end deep-learning BCoP model named Spatio-Temporal feature Auto-Selective (STAS) model to select optimal ST regularity from EC via the ST Feature-selective Mechanisms (SFM/TFM).
no code implementations • 15 Oct 2019 • Xiaoyang Xu, Yiqun Liu, Hanqing Chao, Youcheng Luo, Hai Chu, Lei Chen, Junping Zhang, Leiming Ma
To the best of our knowledge, it is the first expert-free models for bias correction.
1 code implementation • 9 Mar 2019 • Weizhi Ma, Min Zhang, Yue Cao, Woojeong, Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, Xiang Ren
The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.
3 code implementations • 25 Sep 2018 • Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua
Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
no code implementations • COLING 2016 • Meng Zhang, Yang Liu, Huanbo Luan, Yiqun Liu, Maosong Sun
Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains.
no code implementations • 11 Feb 2015 • Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma
In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis.