Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第5页

cwl_eval: An Evaluation Tool for Information Retrieval cwl_eval:一个信息检索的评估工具

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331398

L. Azzopardi, Paul Thomas, Alistair Moffat

{"title":"cwl_eval: An Evaluation Tool for Information Retrieval","authors":"L. Azzopardi, Paul Thomas, Alistair Moffat","doi":"10.1145/3331184.3331398","DOIUrl":"https://doi.org/10.1145/3331184.3331398","url":null,"abstract":"We present a tool (\"cwl_eval\") which unifies many metrics typically used to evaluate information retrieval systems using test collections. In the CWL framework metrics are specified via a single function which can be used to derive a number of related measurements: Expected Utility per item, Expected Total Utility, Expected Cost per item, Expected Total Cost, and Expected Depth. The CWL framework brings together several independent approaches for measuring the quality of a ranked list, and provides a coherent user model-based framework for developing measures based on utility (gain) and cost. Here we outline the CWL measurement framework; describe the cwl_eval architecture; and provide examples of how to use it. We provide implementations of a number of recent metrics, including Time Biased Gain, U-Measure, Bejewelled Measure, and the Information Foraging Based Measure, as well as previous metrics such as Precision, Average Precision, Discounted Cumulative Gain, Rank-Biased Precision, and INST. By providing state-of-the-art and traditional metrics within the same framework, we promote a standardised approach to evaluating search effectiveness.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88053218","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: 22

One-Class Order Embedding for Dependency Relation Prediction 依赖关系预测的单类顺序嵌入

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331249

Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo

{"title":"One-Class Order Embedding for Dependency Relation Prediction","authors":"Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo","doi":"10.1145/3331184.3331249","DOIUrl":"https://doi.org/10.1145/3331184.3331249","url":null,"abstract":"Learning the dependency relations among entities and the hierarchy formed by these relations by mapping entities into some order embedding space can effectively enable several important applications, including knowledge base completion and prerequisite relations prediction. Nevertheless, it is very challenging to learn a good order embedding due to the existence of partial ordering and missing relations in the observed data. Moreover, most application scenarios do not provide non-trivial negative dependency relation instances. We therefore propose a framework that performs dependency relation prediction by exploring both rich semantic and hierarchical structure information in the data. In particular, we propose several negative sampling strategies based on graph-specific centrality properties, which supplement the positive dependency relations with appropriate negative samples to effectively learn order embeddings. This research not only addresses the needs of automatically recovering missing dependency relations, but also unravels dependencies among entities using several real-world datasets, such as course dependency hierarchy involving course prerequisite relations, job hierarchy in organizations, and paper citation hierarchy. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the prediction accuracy as well as to gain insights using the learned order embedding.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86802197","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: 5

Training Streaming Factorization Machines with Alternating Least Squares 交替最小二乘训练流分解机

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331374

Xueyu Mao, Saayan Mitra, Sheng Li

引用次数: 0

Finding Camouflaged Needle in a Haystack?: Pornographic Products Detection via Berrypicking Tree Model 在干草堆里找到伪装的针?:基于berrypkingtree模型的色情产品检测

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331197

Guoxiu He, Yangyang Kang, Zhe Gao, Zhuoren Jiang, Changlong Sun, Xiaozhong Liu, Wei Lu, Qiong Zhang, Luo Si

{"title":"Finding Camouflaged Needle in a Haystack?: Pornographic Products Detection via Berrypicking Tree Model","authors":"Guoxiu He, Yangyang Kang, Zhe Gao, Zhuoren Jiang, Changlong Sun, Xiaozhong Liu, Wei Lu, Qiong Zhang, Luo Si","doi":"10.1145/3331184.3331197","DOIUrl":"https://doi.org/10.1145/3331184.3331197","url":null,"abstract":"It is an important and urgent research problem for decentralized eCommerce services, e.g., eBay, eBid, and Taobao, to detect illegal products, e.g., unclassified pornographic products. However, it is a challenging task as some sellers may utilize and change camouflaged text to deceive the current detection algorithms. In this study, we propose a novel task to dynamically locate the pornographic products from very large product collections. Unlike prior product classification efforts focusing on textual information, the proposed model, BerryPIcking TRee MoDel (BIRD), utilizes both product textual content and buyers' seeking behavior information as berrypicking trees. In particular, the BIRD encodes both semantic information with respect to all branches sequence and the overall latent buyer intent during the whole seeking process. An extensive set of experiments have been conducted to demonstrate the advantage of the proposed model against alternative solutions. To facilitate further research of this practical and important problem, the codes and buyers' seeking behavior data have been made publicly available1.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79906582","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: 9

Investigating the Interplay Between Searchers' Privacy Concerns and Their Search Behavior 调查搜索者的隐私问题和他们的搜索行为之间的相互作用

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331280

Steven Zimmerman, Alistair Thorpe, C. Fox, Udo Kruschwitz

{"title":"Investigating the Interplay Between Searchers' Privacy Concerns and Their Search Behavior","authors":"Steven Zimmerman, Alistair Thorpe, C. Fox, Udo Kruschwitz","doi":"10.1145/3331184.3331280","DOIUrl":"https://doi.org/10.1145/3331184.3331280","url":null,"abstract":"Privacy concerns are becoming a dominant focus in search applications, thus there is a growing need to understand implications of efforts to address these concerns. Our research investigates a search system with privacy warning labels, an approach inspired by decision making research on food nutrition labels. This approach is designed to alert users to potential privacy threats in their search for information as one possible avenue to address privacy concerns. Our primary goal is to understand the extent to which attitudes towards privacy are linked to behaviors that protect privacy. In the present study, participants were given a set of fact-based decision tasks from the domain of health search. Participants were rotated through variations of search engine results pages (SERPs) including a SERP with a privacy warning light system. Lastly, participants completed a survey to capture attitudes towards privacy, behaviors to protect privacy, and other demographic information. In addition to the comparison of interactive search behaviors of a privacy warning SERP with a control SERP, we compared self-report privacy measures with interactive search behaviors. Participants reported strong concerns around privacy of health information while simultaneously placing high importance on the correctness of this information. Analysis of our interactive experiment and self-report privacy measures indicate that 1) choice of privacy-protective browsers has a significant link to privacy attitudes and privacy-protective behaviors in a SERP and 2) there are no significant links between reported concerns towards privacy and recorded behavior in an information retrieval system with warnings that enable users to protect their privacy.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83852980","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: 11

A Context-based Framework for Resource Citation Classification in Scientific Literatures 基于上下文的科学文献资源引文分类框架

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331348

He Zhao, Zhunchen Luo, Chong Feng, Yuming Ye

引用次数: 11

Document Distance Metric Learning in an Interactive Exploration Process 交互式探索过程中的文档距离度量学习

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331420

Marco Wrzalik

引用次数: 0

On Anonymous Commenting: A Greedy Approach to Balance Utilization and Anonymity for Instagram Users 匿名评论:Instagram用户平衡利用率和匿名性的贪婪方法

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331364

Arian Askari, Asal Jalilvand, Mahmood Neshati

引用次数: 2

Teach Machine How to Read: Reading Behavior Inspired Relevance Estimation 教机器如何阅读:阅读行为启发相关性估计

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331205

Xiangsheng Li, Jiaxin Mao, Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma

{"title":"Teach Machine How to Read: Reading Behavior Inspired Relevance Estimation","authors":"Xiangsheng Li, Jiaxin Mao, Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma","doi":"10.1145/3331184.3331205","DOIUrl":"https://doi.org/10.1145/3331184.3331205","url":null,"abstract":"Retrieval models aim to estimate the relevance of a document to a certain query. Although existing retrieval models have gained much success in both deepening our understanding of information seeking behavior and constructing practical retrieval systems (e.g. Web search engines), we have to admit that the models work in a rather different manner than how humans make relevance judgments. In this paper, we aim to reexamine the existing models as well as to propose new ones based on the findings in how human read documents during relevance judgment. First, we summarize a number of reading heuristics from practical user behavior patterns, which are categorized into implicit and explicit heuristics. By reviewing a variety of existing retrieval models, we find that most of them only satisfy a part of these reading heuristics. To evaluate the effectiveness of each heuristic, we conduct an ablation study and find that most heuristics have positive impacts on retrieval performance. We further integrate all the effective heuristics into a new retrieval model named Reading Inspired Model (RIM). Specifically, implicit reading heuristics are incorporated into the model framework and explicit reading heuristics are modeled as a Markov Decision Process and learned by reinforcement learning. Experimental results on a large-scale public available benchmark dataset and two test sets from NTCIR WWW tasks show that RIM outperforms most existing models, which illustrates the effectiveness of the reading heuristics. We believe that this work contributes to constructing retrieval models with both higher retrieval performance and better explainability.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88639624","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: 26

Multi-Level Matching Networks for Text Matching 用于文本匹配的多级匹配网络

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331276

Chunlin Xu, Zhiwei Lin, Shengli Wu, Hui Wang

{"title":"Multi-Level Matching Networks for Text Matching","authors":"Chunlin Xu, Zhiwei Lin, Shengli Wu, Hui Wang","doi":"10.1145/3331184.3331276","DOIUrl":"https://doi.org/10.1145/3331184.3331276","url":null,"abstract":"Text matching aims to establish the matching relationship between two texts. It is an important operation in some information retrieval related tasks such as question duplicate detection, question answering, and dialog systems. Bidirectional long short term memory (BiLSTM) coupled with attention mechanism has achieved state-of-the-art performance in text matching. A major limitation of existing works is that only high level contextualized word representations are utilized to obtain word level matching results without considering other levels of word representations, thus resulting in incorrect matching decisions for cases where two words with different meanings are very close in high level contextualized word representation space. Therefore, instead of making decisions utilizing single level word representations, a multi-level matching network (MMN) is proposed in this paper for text matching, which utilizes multiple levels of word representations to obtain multiple word level matching results for final text level matching decision. Experimental results on two widely used benchmarks, SNLI and Scaitail, show that the proposed MMN achieves the state-of-the-art performance.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86432677","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: 8