European Conference on Information Retrieval最新文献_第3页

A Deep Learning Approach for Selective Relevance Feedback 选择性相关性反馈的深度学习方法

European Conference on Information Retrieval Pub Date : 2024-01-20 DOI: 10.48550/arXiv.2401.11198

S. Datta, Debasis Ganguly, Sean MacAvaney, Derek Greene

{"title":"A Deep Learning Approach for Selective Relevance Feedback","authors":"S. Datta, Debasis Ganguly, Sean MacAvaney, Derek Greene","doi":"10.48550/arXiv.2401.11198","DOIUrl":"https://doi.org/10.48550/arXiv.2401.11198","url":null,"abstract":"Pseudo-relevance feedback (PRF) can enhance average retrieval effectiveness over a sufficiently large number of queries. However, PRF often introduces a drift into the original information need, thus hurting the retrieval effectiveness of several queries. While a selective application of PRF can potentially alleviate this issue, previous approaches have largely relied on unsupervised or feature-based learning to determine whether a query should be expanded. In contrast, we revisit the problem of selective PRF from a deep learning perspective, presenting a model that is entirely data-driven and trained in an end-to-end manner. The proposed model leverages a transformer-based bi-encoder architecture. Additionally, to further improve retrieval effectiveness with this selective PRF approach, we make use of the model's confidence estimates to combine the information from the original and expanded queries. In our experiments, we apply this selective feedback on a number of different combinations of ranking and feedback models, and show that our proposed approach consistently improves retrieval effectiveness for both sparse and dense ranking models, with the feedback models being either sparse, dense or generative.","PeriodicalId":126309,"journal":{"name":"European Conference on Information Retrieval","volume":"56 1","pages":"189-204"},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140502180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ranking Heterogeneous Search Result Pages using the Interactive Probability Ranking Principle 利用交互式概率排序原则对异构搜索结果页面进行排序

European Conference on Information Retrieval Pub Date : 2024-01-16 DOI: 10.1007/978-3-031-56060-6_7

Kanaad Pathak, Leif Azzopardi, Martin Halvey

引用次数: 0

A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR 金发姑娘的可重复性研究：为 TAR 对 BERT 进行恰到好处的调整

European Conference on Information Retrieval Pub Date : 2024-01-16 DOI: 10.48550/arXiv.2401.08104

Xinyu Mao, B. Koopman, G. Zuccon

{"title":"A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR","authors":"Xinyu Mao, B. Koopman, G. Zuccon","doi":"10.48550/arXiv.2401.08104","DOIUrl":"https://doi.org/10.48550/arXiv.2401.08104","url":null,"abstract":"Screening documents is a tedious and time-consuming aspect of high-recall retrieval tasks, such as compiling a systematic literature review, where the goal is to identify all relevant documents for a topic. To help streamline this process, many Technology-Assisted Review (TAR) methods leverage active learning techniques to reduce the number of documents requiring review. BERT-based models have shown high effectiveness in text classification, leading to interest in their potential use in TAR workflows. In this paper, we investigate recent work that examined the impact of further pre-training epochs on the effectiveness and efficiency of a BERT-based active learning pipeline. We first report that we could replicate the original experiments on two specific TAR datasets, confirming some of the findings: importantly, that further pre-training is critical to high effectiveness, but requires attention in terms of selecting the correct training epoch. We then investigate the generalisability of the pipeline on a different TAR task, that of medical systematic reviews. In this context, we show that there is no need for further pre-training if a domain-specific BERT backbone is used within the active learning pipeline. This finding provides practical implications for using the studied active learning pipeline within domain-specific TAR tasks.","PeriodicalId":126309,"journal":{"name":"European Conference on Information Retrieval","volume":"78 1","pages":"132-146"},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140505786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems 揭示 Top-N 指标对推荐系统优化的隐性影响

European Conference on Information Retrieval Pub Date : 2024-01-16 DOI: 10.48550/arXiv.2401.08444

Lukas Wegmeth, Tobias Vente, Lennart Purucker

{"title":"Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems","authors":"Lukas Wegmeth, Tobias Vente, Lennart Purucker","doi":"10.48550/arXiv.2401.08444","DOIUrl":"https://doi.org/10.48550/arXiv.2401.08444","url":null,"abstract":"The hyperparameters of recommender systems for top-n predictions are typically optimized to enhance the predictive performance of algorithms. Thereby, the optimization algorithm, e.g., grid search or random search, searches for the best hyperparameter configuration according to an optimization-target metric, like nDCG or Precision. In contrast, the optimized algorithm, internally optimizes a different loss function during training, like squared error or cross-entropy. To tackle this discrepancy, recent work focused on generating loss functions better suited for recommender systems. Yet, when evaluating an algorithm using a top-n metric during optimization, another discrepancy between the optimization-target metric and the training loss has so far been ignored. During optimization, the top-n items are selected for computing a top-n metric; ignoring that the top-n items are selected from the recommendations of a model trained with an entirely different loss function. Item recommendations suitable for optimization-target metrics could be outside the top-n recommended items; hiddenly impacting the optimization performance. Therefore, we were motivated to analyze whether the top-n items are optimal for optimization-target top-n metrics. In pursuit of an answer, we exhaustively evaluate the predictive performance of 250 selection strategies besides selecting the top-n. We extensively evaluate each selection strategy over twelve implicit feedback and eight explicit feedback data sets with eleven recommender systems algorithms. Our results show that there exist selection strategies other than top-n that increase predictive performance for various algorithms and recommendation domains. However, the performance of the top ~43% of selection strategies is not significantly different. We discuss the impact of our findings on optimization and re-ranking in recommender systems and feasible solutions.","PeriodicalId":126309,"journal":{"name":"European Conference on Information Retrieval","volume":"51 5","pages":"140-156"},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140506371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT 使用 GPT 生成与语言无关的 MCQ 的新型多阶段提示方法

European Conference on Information Retrieval Pub Date : 2024-01-13 DOI: 10.48550/arXiv.2401.07098

S. Maity, Aniket Deroy, Sudeshna Sarkar

引用次数: 0

CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification CrisisKAN：用于危机事件分类的注入知识且可解释的多模态注意力网络

European Conference on Information Retrieval Pub Date : 2024-01-11 DOI: 10.48550/arXiv.2401.06194

Shubham Gupta, Nandini Saini, Suman Kundu, Debasis Das

{"title":"CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification","authors":"Shubham Gupta, Nandini Saini, Suman Kundu, Debasis Das","doi":"10.48550/arXiv.2401.06194","DOIUrl":"https://doi.org/10.48550/arXiv.2401.06194","url":null,"abstract":"Pervasive use of social media has become the emerging source for real-time information (like images, text, or both) to identify various events. Despite the rapid growth of image and text-based event classification, the state-of-the-art (SOTA) models find it challenging to bridge the semantic gap between features of image and text modalities due to inconsistent encoding. Also, the black-box nature of models fails to explain the model's outcomes for building trust in high-stakes situations such as disasters, pandemic. Additionally, the word limit imposed on social media posts can potentially introduce bias towards specific events. To address these issues, we proposed CrisisKAN, a novel Knowledge-infused and Explainable Multimodal Attention Network that entails images and texts in conjunction with external knowledge from Wikipedia to classify crisis events. To enrich the context-specific understanding of textual information, we integrated Wikipedia knowledge using proposed wiki extraction algorithm. Along with this, a guided cross-attention module is implemented to fill the semantic gap in integrating visual and textual data. In order to ensure reliability, we employ a model-specific approach called Gradient-weighted Class Activation Mapping (Grad-CAM) that provides a robust explanation of the predictions of the proposed model. The comprehensive experiments conducted on the CrisisMMD dataset yield in-depth analysis across various crisis-specific tasks and settings. As a result, CrisisKAN outperforms existing SOTA methodologies and provides a novel view in the domain of explainable multimodal event classification.","PeriodicalId":126309,"journal":{"name":"European Conference on Information Retrieval","volume":"6 2","pages":"18-33"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140509922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant 生态贤者助手：打造多模式植物护理对话助手

European Conference on Information Retrieval Pub Date : 2024-01-10 DOI: 10.48550/arXiv.2401.06807

Mohit Tomar, Abhisek Tiwari, Tulika Saha, Prince Jha, Sriparna Saha

{"title":"An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant","authors":"Mohit Tomar, Abhisek Tiwari, Tulika Saha, Prince Jha, Sriparna Saha","doi":"10.48550/arXiv.2401.06807","DOIUrl":"https://doi.org/10.48550/arXiv.2401.06807","url":null,"abstract":"In recent times, there has been an increasing awareness about imminent environmental challenges, resulting in people showing a stronger dedication to taking care of the environment and nurturing green life. The current $19.6 billion indoor gardening industry, reflective of this growing sentiment, not only signifies a monetary value but also speaks of a profound human desire to reconnect with the natural world. However, several recent surveys cast a revealing light on the fate of plants within our care, with more than half succumbing primarily due to the silent menace of improper care. Thus, the need for accessible expertise capable of assisting and guiding individuals through the intricacies of plant care has become paramount more than ever. In this work, we make the very first attempt at building a plant care assistant, which aims to assist people with plant(-ing) concerns through conversations. We propose a plant care conversational dataset named Plantational, which contains around 1K dialogues between users and plant care experts. Our end-to-end proposed approach is two-fold : (i) We first benchmark the dataset with the help of various large language models (LLMs) and visual language model (VLM) by studying the impact of instruction tuning (zero-shot and few-shot prompting) and fine-tuning techniques on this task; (ii) finally, we build EcoSage, a multi-modal plant care assisting dialogue generation framework, incorporating an adapter-based modality infusion using a gated mechanism. We performed an extensive examination (both automated and manual evaluation) of the performance exhibited by various LLMs and VLM in the generation of the domain-specific dialogue responses to underscore the respective strengths and weaknesses of these diverse models.","PeriodicalId":126309,"journal":{"name":"European Conference on Information Retrieval","volume":"47 15","pages":"318-332"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Living Lab Evaluation for Life and Social Sciences Search Platforms - LiLAS at CLEF 2021 生活实验室评估生命和社会科学搜索平台- LiLAS在CLEF 2021

European Conference on Information Retrieval Pub Date : 2023-10-05 DOI: 10.1007/978-3-030-72240-1_77

Philipp Schaer, Johann Schaible, Leyla Jael García Castro

引用次数: 1

Theoretical Analysis on the Efficiency of Interleaved Comparisons 交错比较效率的理论分析

European Conference on Information Retrieval Pub Date : 2023-05-31 DOI: 10.1007/978-3-031-28244-7_29

Kojiro Iizuka, Hajime Morita, Makoto P. Kato

引用次数: 0

Privacy-Preserving Fair Item Ranking 隐私保护公平项目排名

European Conference on Information Retrieval Pub Date : 2023-03-06 DOI: 10.48550/arXiv.2303.02916

Jiajun Sun, Sikha Pentyala, Martine De Cock, G. Farnadi

引用次数: 1