Proceedings of the 2019 Conference on Human Information Interaction and Retrieval最新文献_第7页

How Relevance Feedback is Framed Affects User Experience, but not Behaviour 相关反馈是如何影响用户体验而非行为的

Proceedings of the 2019 Conference on Human Information Interaction and Retrieval Pub Date : 2019-03-08 DOI: 10.1145/3295750.3298957

Dhruv Tripathi, A. Medlar, D. Glowacka

引用次数: 1

From Delivering Facts to Generating Emotions: The Complex Relationship between Museums and Information 从传递事实到产生情感:博物馆与信息的复杂关系

Proceedings of the 2019 Conference on Human Information Interaction and Retrieval Pub Date : 2019-03-08 DOI: 10.1145/3295750.3300047

Daniela Petrelli

{"title":"From Delivering Facts to Generating Emotions: The Complex Relationship between Museums and Information","authors":"Daniela Petrelli","doi":"10.1145/3295750.3300047","DOIUrl":"https://doi.org/10.1145/3295750.3300047","url":null,"abstract":"Motivation : The past 25 years have seen a constant increase in the use of information technology to deliver digital content in cultural heritage settings. Museums have experimented with multimedia PCs, PDAs and phones, table-tops, Google Glass and now VR. The aim has always been to provide more information despite the fact that only a minority of visitors consumes the information on offer. Failing to engage visitors should direct our concerns on the 'receiving' side rather than on the 'delivering' side, that is to say to look at the visitors' experience rather than the technology [1]. Problem statement : The problem lays in the way the interactive experience is designed: too often it is as an 'add on' to the physical exhibition rather than an integral part of the experience. The emerging Internet of Things bridges the gap between the physical and the digital and enables to seamless integrate the digital content with the material collection or the historical space. Via embedded technology it is possible to collect and exploit visitors' data opening up new possibilities to create engaging and personalised visitors' experiences onsite and online. Approach : Using a number of case studies of exhibitions and installations used by over 20,000 visitors across Europe, I will show how the interaction with information can be designed as part of multisensory exhibitions that engages the visitor at many levels and generate emotion. The approach is collaborative and requires the equal contribution of technologists, designers and content experts throughout the whole process, from early conception to the final implementation. The response of the visitors goes well beyond expectations opening up new opportunities for long-term visitors' engagement.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115186499","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

Challenges and Supports for Accessing Open Government Datasets: Data Guide for Better Open Data Access and Uses 访问开放政府数据集的挑战和支持:更好地访问和使用开放数据的数据指南

Proceedings of the 2019 Conference on Human Information Interaction and Retrieval Pub Date : 2019-03-01 DOI: 10.1145/3295750.3298958

Fanghui Xiao, Daqing He, Yu Chi, Wei Jeng, C. Tomer

引用次数: 10

Reading Protocol: Understanding what has been Read in Interactive Information Retrieval Tasks 阅读协议:了解交互式信息检索任务中已阅读的内容

Proceedings of the 2019 Conference on Human Information Interaction and Retrieval Pub Date : 2019-02-12 DOI: 10.1145/3295750.3298921

Daniel Hienert, Dagmar Kern, M. Mitsui, C. Shah, N. Belkin

{"title":"Reading Protocol: Understanding what has been Read in Interactive Information Retrieval Tasks","authors":"Daniel Hienert, Dagmar Kern, M. Mitsui, C. Shah, N. Belkin","doi":"10.1145/3295750.3298921","DOIUrl":"https://doi.org/10.1145/3295750.3298921","url":null,"abstract":"In Interactive Information Retrieval (IIR) experiments the user's gaze motion on web pages is often recorded with eye tracking. The data is used to analyze gaze behavior or to identify Areas of Interest (AOI) the user has looked at. So far, tools for analyzing eye tracking data have certain limitations in supporting the analysis of gaze behavior in IIR experiments. Experiments often consist of a huge number of different visited web pages. In existing analysis tools the data can only be analyzed in videos or images and AOIs for every single web page have to be specified by hand, in a very time consuming process. In this work, we propose the reading protocol software which breaks eye tracking data down to the textual level by considering the HTML structure of the web pages. This has a lot of advantages for the analyst. First and foremost, it can easily be identified on a large scale what has actually been viewed and read on the stimuli pages by the subjects. Second, the web page structure can be used to filter to AOIs. Third, gaze data of multiple users can be presented on the same page, and fourth, fixation times on text can be exported and further processed in other tools. We present the software, its validation, and example use cases with data from three existing IIR experiments.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126909241","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

Answering Comparative Questions: Better than Ten-Blue-Links? 回答比较性问题:比十个蓝链接更好?

Proceedings of the 2019 Conference on Human Information Interaction and Retrieval Pub Date : 2019-01-15 DOI: 10.1145/3295750.3298916

Matthias Schildwächter, Alexander Bondarenko, Julian Zenker, Matthias Hagen, Chris Biemann, Alexander Panchenko

引用次数: 24

User Intent Prediction in Information-seeking Conversations 信息搜索对话中的用户意图预测

Proceedings of the 2019 Conference on Human Information Interaction and Retrieval Pub Date : 2019-01-11 DOI: 10.1145/3295750.3298924

Chen Qu, Liu Yang, W. Bruce Croft, Yongfeng Zhang, Johanne R. Trippas, Minghui Qiu

{"title":"User Intent Prediction in Information-seeking Conversations","authors":"Chen Qu, Liu Yang, W. Bruce Croft, Yongfeng Zhang, Johanne R. Trippas, Minghui Qiu","doi":"10.1145/3295750.3298924","DOIUrl":"https://doi.org/10.1145/3295750.3298924","url":null,"abstract":"Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114916876","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: 77

Answer Interaction in Non-factoid Question Answering Systems 非事实问答系统中的答案交互

Proceedings of the 2019 Conference on Human Information Interaction and Retrieval Pub Date : 2019-01-11 DOI: 10.1145/3295750.3298946

Chen Qu, Liu Yang, W. Bruce Croft, Falk Scholer, Yongfeng Zhang

引用次数: 19

Understanding Mobile Search Task Relevance and User Behaviour in Context 理解移动搜索任务的相关性和用户行为

Proceedings of the 2019 Conference on Human Information Interaction and Retrieval Pub Date : 2018-12-17 DOI: 10.1145/3295750.3298923

Mohammad Aliannejadi, Morgan Harvey, Luca Costa, Matthew Pointon, F. Crestani

{"title":"Understanding Mobile Search Task Relevance and User Behaviour in Context","authors":"Mohammad Aliannejadi, Morgan Harvey, Luca Costa, Matthew Pointon, F. Crestani","doi":"10.1145/3295750.3298923","DOIUrl":"https://doi.org/10.1145/3295750.3298923","url":null,"abstract":"Improvements in mobile technologies have led to a dramatic change in how and when people access and use information, and is having a profound impact on how users address their daily information needs. Smart phones are rapidly becoming our main method of accessing information and are frequently used to perform \"on-the-go'' search tasks. As research into information retrieval continues to evolve, evaluating search behaviour in context is relatively new. Previous research has studied the effects of context through either self-reported diary studies or quantitative log analysis; however, neither approach is able to accurately capture context of use at the time of searching. In this study, we aim to gain a better understanding of task relevance and search behaviour via a task-based user study (n=31) employing a bespoke Android app. The app allowed us to accurately capture the user's context when completing tasks at different times of the day over the period of a week. Through analysis of the collected data, we gain a better understanding of how using smart phones on the go impacts search behaviour, search performance and task relevance and whether or not the actual context is an important factor.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117043015","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: 24