Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第10页

EDIndex: Enabling Fast Data Queries in Edge Storage Systems EDIndex:在边缘存储系统中实现快速数据查询

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591676

Qiang He, Siyu Tan, Feifei Chen, Xiaolong Xu, Lianyong Qi, X. Hei, Hai Jin, Yun Yang

{"title":"EDIndex: Enabling Fast Data Queries in Edge Storage Systems","authors":"Qiang He, Siyu Tan, Feifei Chen, Xiaolong Xu, Lianyong Qi, X. Hei, Hai Jin, Yun Yang","doi":"10.1145/3539618.3591676","DOIUrl":"https://doi.org/10.1145/3539618.3591676","url":null,"abstract":"In an edge storage system, popular data can be stored on edge servers to enable low-latency data retrieval for nearby users. Suffering from constrained storage capacities, edge servers must process users' data requests collaboratively. For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. First, we introduce a new index structure named Counting Bloom Filter (CBF) tree for facilitating edge data queries. Then, to improve query performance, we enhance EDIndex with a novel index structure named hierarchical Counting Bloom Filter (HCBF) tree. In EDIndex, each edge server maintains an HCBF tree that indexes the data stored on nearby edge servers to facilitate data sourcing between edge servers at the edge. The results of extensive experiments conducted on an edge storage system comprised of 90 edge servers demonstrate that EDIndex 1) takes up to 8.8x less time to answer edge data queries compared with state-of-the-art edge indexing systems; and 2) can be implemented in practice with a high query accuracy at low initialization and maintenance overheads.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116897555","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: 7

Alleviating Matching Bias in Marketing Recommendations 缓解营销推荐中的匹配偏差

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591854

Junpeng Fang, Qing Cui, Gongduo Zhang, Caizhi Tang, Lihong Gu, Longfei Li, Jinjie Gu, Jun Zhou, Fei Wu

{"title":"Alleviating Matching Bias in Marketing Recommendations","authors":"Junpeng Fang, Qing Cui, Gongduo Zhang, Caizhi Tang, Lihong Gu, Longfei Li, Jinjie Gu, Jun Zhou, Fei Wu","doi":"10.1145/3539618.3591854","DOIUrl":"https://doi.org/10.1145/3539618.3591854","url":null,"abstract":"In marketing recommendations, the campaign organizers will distribute coupons to users to encourage consumption. In general, a series of strategies are employed to interfere with the coupon distribution process, leading to a growing imbalance between user-coupon interactions, resulting in a bias in the estimation of conversion probabilities. We refer to the estimation bias as the matching bias. In this paper, we explore how to alleviate the matching bias from the causal-effect perspective. We regard the historical distributions of users and coupons over each other as confounders and characterize the matching bias as a confounding effect to reveal and eliminate the spurious correlations between user-coupon representations and conversion probabilities. Then we propose a new training paradigm named De-Matching Bias Recommendation (DMBR) to remove the confounding effects during model training via the backdoor adjustment. We instantiate DMBR on two representative models: DNN and MMOE, and conduct extensive offline and online experiments to demonstrate the effectiveness of our proposed paradigm.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116654563","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: 1

Can Generative LLMs Create Query Variants for Test Collections? An Exploratory Study 生成法学硕士可以为测试集合创建查询变体吗?一项探索性研究

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591960

An Exploratory Study, L. Gallagher, Marwah Alaofi, M. Sanderson, Falk Scholer

引用次数: 2

Aligning Distillation For Cold-start Item Recommendation 冷启动校准蒸馏项目推荐

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591732

Feiran Huang, Zefan Wang, Xiao Huang, Yu-hong Qian, Zhetao Li, Hao Chen

{"title":"Aligning Distillation For Cold-start Item Recommendation","authors":"Feiran Huang, Zefan Wang, Xiao Huang, Yu-hong Qian, Zhetao Li, Hao Chen","doi":"10.1145/3539618.3591732","DOIUrl":"https://doi.org/10.1145/3539618.3591732","url":null,"abstract":"Recommending cold items in recommendation systems is a longstanding challenge due to the inherent differences between warm items, which are recommended based on user behavior, and cold items, which are recommended based on content features. To tackle this, generative models generate synthetic embeddings from content features, while dropout models enhance the robustness of the recommendation system by randomly dropping behavioral embeddings during training. However, these models primarily focus on handling the recommendation of cold items, but do not effectively address the differences between warm and cold recommendations. As a result, generative models may over-recommend either warm or cold items, neglecting the other type, and dropout models may negatively impact warm item recommendations. To address this, we propose the Aligning Distillation (ALDI) framework, which leverages warm items as \"teachers\" to transfer their behavioral information to cold items, referred to as \"students\". ALDI aligns the students with the teachers by comparing the differences in their recommendation characters, using tailored rating distribution aligning, ranking aligning, and identification aligning losses to narrow these differences. Furthermore, ALDI incorporates a teacher-qualifying weighting structure to prevent students from learning inaccurate information from unreliable teachers. Experiments on three datasets show that our approach outperforms state-of-the-art baselines in terms of overall, warm, and cold recommendation performance with three different recommendation backbones.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114710602","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: 3

A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System 基于模型不可知的推荐系统点击率预测人气偏差训练框架

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591939

Fan Zhang, Qijie Shen

引用次数: 0

Cross-Market Product-Related Question Answering 跨市场产品相关问题解答

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591658

Negin Ghasemi, Mohammad Aliannejadi, Hamed Bonab, E. Kanoulas, Arjen P. de Vries, J. Allan, D. Hiemstra

{"title":"Cross-Market Product-Related Question Answering","authors":"Negin Ghasemi, Mohammad Aliannejadi, Hamed Bonab, E. Kanoulas, Arjen P. de Vries, J. Allan, D. Hiemstra","doi":"10.1145/3539618.3591658","DOIUrl":"https://doi.org/10.1145/3539618.3591658","url":null,"abstract":"Online shops such as Amazon, eBay, and Etsy continue to expand their presence in multiple countries, creating new resource-scarce marketplaces with thousands of items. We consider a marketplace to be resource-scarce when only limited user-generated data is available about the products (e.g., ratings, reviews, and product-related questions). In such a marketplace, an information retrieval system is less likely to help users find answers to their questions about the products. As a result, questions posted online may go unanswered for extended periods. This study investigates the impact of using available data in a resource-rich marketplace to answer new questions in a resource-scarce marketplace, a new problem we call cross-market question answering. To study this problem's potential impact, we collect and annotate a new dataset, XMarket-QA, from Amazon's UK (resource-scarce) and US (resource-rich) local marketplaces. We conduct a data analysis to understand the scope of the cross-market question-answering task. This analysis shows a temporal gap of almost one year between the first question answered in the UK marketplace and the US marketplace. Also, it shows that the first question about a product is posted in the UK marketplace only when 28 questions, on average, have already been answered about the same product in the US marketplace. Human annotations demonstrate that, on average, 65% of the questions in the UK marketplace can be answered within the US marketplace, supporting the concept of cross-market question answering. Inspired by these findings, we develop a new method, CMJim, which utilizes product similarities across marketplaces in the training phase for retrieving answers from the resource-rich marketplace that can be used to answer a question in the resource-scarce marketplace. Our evaluations show CMJim's significant improvement compared to competitive baselines.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124797822","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

MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter MetroScope:通过Twitter实时检测和分析地铁相关威胁和事件的先进系统

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591807

Jianfeng He, Syuan-Ying Wu, Abdulaziz Alhamadani, Chih-Fang Chen, Wen-Fang Lu, Chang-Tien Lu, David Solnick, Yanlin Li

{"title":"MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter","authors":"Jianfeng He, Syuan-Ying Wu, Abdulaziz Alhamadani, Chih-Fang Chen, Wen-Fang Lu, Chang-Tien Lu, David Solnick, Yanlin Li","doi":"10.1145/3539618.3591807","DOIUrl":"https://doi.org/10.1145/3539618.3591807","url":null,"abstract":"Metro systems are vital to our daily lives, but they face safety or reliability challenges, such as criminal activities or infrastructure disruptions, respectively. Real-time threat detection and analysis are crucial to ensure their safety and reliability. Although many existing systems use Twitter to detect metro-related threats or events in real-time, they have limitations in event analysis and system maintenance. Specifically, they cannot analyze event development, or prioritize events from numerous tweets. Besides, their users are required to continuously monitor system notifications, use inefficient content retrieval methods, and perform detailed system maintenance. We addressed those issues by developing the MetroScope system, a real-time threat/event detection system applied to Washington D.C. metro system. MetroScope can automatically analyze event development, prioritize events based on urgency, send emergency notifications via emails, provide efficient content retrieval, and self-maintain the system. Our MetroScope system is now available at http://orion.nvc.cs.vt.edu:5000/, with a video (https://www.youtube.com/watch?v=vKIK9M60-J8) introducing its features and instructions. MetroScope is a significant advancement in enhancing the safety and reliability of metro systems.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121754796","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: 1

TIB AV-Analytics: A Web-based Platform for Scholarly Video Analysis and Film Studies TIB AV-Analytics:一个学术视频分析和电影研究的网络平台

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591820

Matthias Springstein, Markos Stamatakis, Margret Plank, Julian Sittel, Roman Mauer, Oksana Bulgakowa, R. Ewerth, Eric Müller-Budack

{"title":"TIB AV-Analytics: A Web-based Platform for Scholarly Video Analysis and Film Studies","authors":"Matthias Springstein, Markos Stamatakis, Margret Plank, Julian Sittel, Roman Mauer, Oksana Bulgakowa, R. Ewerth, Eric Müller-Budack","doi":"10.1145/3539618.3591820","DOIUrl":"https://doi.org/10.1145/3539618.3591820","url":null,"abstract":"Video analysis platforms that integrate automatic solutions for multimedia and information retrieval enable various applications in many disciplines including film and media studies, communication science, and education. However, current platforms for video analysis either focus on manual annotations or include only a few tools for automatic content analysis. In this paper, we present a novel web-based video analysis platform called TIB AV-Analytics (TIB-AV-A). Unlike previous platforms, TIB-AV-A integrates state-of-the-art approaches in the fields of computer vision, audio analysis, and natural language processing for many relevant video analysis tasks. To facilitate future extensions and to ensure interoperability with existing tools, the video analysis approaches are implemented in a plugin structure with appropriate interfaces and import-export functions. TIB-AV-A leverages modern web technologies to provide users with a responsive and interactive web interface that enables manual annotation and provides access to powerful deep learning tools without a requirement for specific hardware dependencies. Source code and demo are publicly available at: https://service.tib.eu/tibava.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122441190","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

MaxSimE: Explaining Transformer-based Semantic Similarity via Contextualized Best Matching Token Pairs MaxSimE:通过上下文化的最佳匹配令牌对解释基于转换器的语义相似性

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592017

E. Brito, Henri Iser

引用次数: 0

Data-Aware Proxy Hashing for Cross-modal Retrieval 跨模态检索的数据感知代理哈希

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591660

Rong-Cheng Tu, Xian-Ling Mao, Wenjin Ji, Wei Wei, Heyan Huang

{"title":"Data-Aware Proxy Hashing for Cross-modal Retrieval","authors":"Rong-Cheng Tu, Xian-Ling Mao, Wenjin Ji, Wei Wei, Heyan Huang","doi":"10.1145/3539618.3591660","DOIUrl":"https://doi.org/10.1145/3539618.3591660","url":null,"abstract":"Recently, numerous proxy hash code based methods, which sufficiently exploit the label information of data to supervise the training of hashing models, have been proposed. Although these methods have made impressive progress, their generating processes of proxy hash codes are based only on the class information of the dataset or labels of data but do not take the data themselves into account. Therefore, these methods will probably generate some inappropriate proxy hash codes, thus damaging the retrieval performance of the hash models. To solve the aforementioned problem, we propose a novel Data-Aware Proxy Hashing for cross-modal retrieval, called DAPH. Specifically, our proposed method first train a data-aware proxy network that takes the data points, label vectors of data, and the class vectors of the dataset as inputs to generate class-based data-aware proxy hash codes, label-fused image-aware proxy hash codes and label-fused text-aware proxy hash codes. Then, we propose a novel hash loss that exploits the three types of data-aware proxy hash codes to supervise the training of modality-specific hashing networks. After training, DAPH is able to generate discriminate hash codes with the semantic information preserved adequately. Extensive experiments on three benchmark datasets show that the proposed DAPH outperforms the state-of-the-art baselines in cross-modal retrieval tasks.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124947959","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