Search Results for author: Shaoping Ma

Found 27 papers, 15 papers with code

Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently

2 code implementations20 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.

Passage Retrieval Retrieval

Optimizing Dense Retrieval Model Training with Hard Negatives

4 code implementations16 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.

Information Retrieval Representation Learning +1

Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance

5 code implementations2 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.

Information Retrieval Quantization +1

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

4 code implementations12 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.

Constrained Clustering Information Retrieval +3

RepBERT: Contextualized Text Embeddings for First-Stage Retrieval

3 code implementations28 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.

Passage Ranking Retrieval

Towards Representation Alignment and Uniformity in Collaborative Filtering

2 code implementations26 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.

Collaborative Filtering Recommendation Systems

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

1 code implementation9 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.

Explainable Recommendation Knowledge Graphs +1

Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval

1 code implementation11 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.

Ad-Hoc Information Retrieval Domain Adaptation +1

I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval

1 code implementation4 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.

Knowledge Distillation Passage Retrieval +2

Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System

1 code implementation17 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.

EEG Electroencephalogram (EEG) +2

Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

1 code implementation25 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.

Retrieval

Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction

1 code implementation24 Jul 2023 Yifan Wang, Peijie Sun, Min Zhang, Qinglin Jia, Jingjie Li, Shaoping Ma

To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples.

counterfactual

Towards a Better Understanding Human Reading Comprehension with Brain Signals

1 code implementation3 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.

EEG Electroencephalogram (EEG) +5

Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification

no code implementations11 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.

Classification General Classification +2

Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search

no code implementations22 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).

EEG Electroencephalogram (EEG)

Web Search via an Efficient and Effective Brain-Machine Interface

no code implementations14 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.

EEG Electroencephalogram (EEG)

Interpreting Dense Retrieval as Mixture of Topics

no code implementations27 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.

Retrieval

A Survey on Dropout Methods and Experimental Verification in Recommendation

no code implementations5 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.

A Survey on the Fairness of Recommender Systems

no code implementations8 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.

Fairness Recommendation Systems

THUIR2 at NTCIR-16 Session Search (SS) Task

no code implementations1 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.

Language Modelling Learning-To-Rank +1

An Intent Taxonomy of Legal Case Retrieval

no code implementations25 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.

Information Retrieval Retrieval +1

Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback

no code implementations29 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).

Data Augmentation Information Retrieval +4

Evaluation Ethics of LLMs in Legal Domain

no code implementations17 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.

Ethics

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