Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第8页

Mention Recommendation in Twitter with Cooperative Multi-Agent Reinforcement Learning 提到Twitter中使用协同多智能体强化学习的推荐

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331237

Tao Gui, Peng Liu, Qi Zhang, Liang Zhu, Minlong Peng, Yunhua Zhou, Xuanjing Huang

引用次数: 14

Effective Online Evaluation for Web Search 网络搜索的有效在线评估

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331378

Alexey Drutsa, Gleb Gusev, E. Kharitonov, Denis Kulemyakin, P. Serdyukov, I. Yashkov

引用次数: 3

Which Diversity Evaluation Measures Are "Good"? 哪些多样性评估措施是“好的”?

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331215

T. Sakai, Zhaohao Zeng

{"title":"Which Diversity Evaluation Measures Are \"Good\"?","authors":"T. Sakai, Zhaohao Zeng","doi":"10.1145/3331184.3331215","DOIUrl":"https://doi.org/10.1145/3331184.3331215","url":null,"abstract":"This study evaluates 30 IR evaluation measures or their instances, of which nine are for adhoc IR and 21 are for diversified IR, primarily from the viewpoint of whether their preferences of one SERP (search engine result page) over another actually align with users' preferences. The gold preferences were contructed by hiring 15 assessors, who independently examined 1,127 SERP pairs and made preference assessments. Two sets of preference assessments were obtained: one based on a relevance question \"Which SERP is more relevant?'' and the other based on a diversity question \"Which SERP is likely to satisfy a higher number of users?'' To our knowledge, our study is the first to have collected diversity preference assessments in this way and evaluated diversity measures successfully. Our main results are that (a) Popular adhoc IR measures such as nDCG actually align quite well with the gold relevance preferences; and that (b) While the ♯-measures align well with the gold diversity preferences, intent-aware measures perform relatively poorly. Moreover, as by-products of our analysis of existing evaluation measures, we define new adhoc measures called iRBU (intentwise Rank-Biased Utility) and EBR (Expected Blended Ratio); we demonstrate that an instance of iRBU performs as well as nDCG when compared to the gold relevance preferences. On the other hand, the original RBU, a recently-proposed diversity measure, underperforms the best ♯-measures when compared to the gold diversity preferences.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85783501","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: 36

Web Table Extraction, Retrieval and Augmentation Web表提取，检索和增强

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331385

Shuo Zhang, K. Balog

引用次数: 36

Fast Approximate Filtering of Search Results Sorted by Attribute 按属性排序搜索结果的快速近似过滤

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331227

F. M. Nardini, Roberto Trani, Rossano Venturini

{"title":"Fast Approximate Filtering of Search Results Sorted by Attribute","authors":"F. M. Nardini, Roberto Trani, Rossano Venturini","doi":"10.1145/3331184.3331227","DOIUrl":"https://doi.org/10.1145/3331184.3331227","url":null,"abstract":"Several Web search services enable their users with the possibility of sorting the list of results by a specific attribute, e.g., sort \"by price\" in e-commerce. However, sorting the results by attribute could bring marginally relevant results in the top positions thus leading to a poor user experience. This motivates the definition of the relevance-aware filtering problem. This problem asks to remove results from the attribute-sorted list to maximize its final overall relevance. Recently, an optimal solution to this problem has been proposed. However, it has strong limitations in the Web scenario due to its high computational cost. In this paper, we propose ϵ-Filtering: an efficient approximate algorithm with strong approximation guarantees on the relevance of the final list. More precisely, given an allowed approximation error ϵ, the proposed algorithm finds a(1-ϵ)\"optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of ϵ-Filtering against state-of-the-art competitors on two real-world public datasets. Experiments show that ϵ-Filtering achieves the desired levels of effectiveness with a speedup of up to two orders of magnitude with respect to the optimal solution while guaranteeing very small approximation errors.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88444377","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

Learning from Fact-checkers: Analysis and Generation of Fact-checking Language 向事实核查者学习:事实核查语言的分析与生成

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331248

Nguyen Vo, Kyumin Lee

{"title":"Learning from Fact-checkers: Analysis and Generation of Fact-checking Language","authors":"Nguyen Vo, Kyumin Lee","doi":"10.1145/3331184.3331248","DOIUrl":"https://doi.org/10.1145/3331184.3331248","url":null,"abstract":"In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named fact-checkers, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with fact-checking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88057025","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: 51

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":"1 1","pages":""},"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

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331239

Sérgio D. Canuto, Thiago Salles, Thierson Couto, Marcos André Gonçalves

引用次数: 15

Session details: Session 1B: Health and Social Media 会议详情:会议1B:健康和社交媒体

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3349676

Mark D. Smucker

引用次数: 0

Reinforcement Learning for User Intent Prediction in Customer Service Bots 客服机器人中用户意图预测的强化学习

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331370

Cen Chen, Chilin Fu, Xujun Hu, Xiaolu Zhang, Jun Zhou, Xiaolong Li, F. S. Bao

引用次数: 15