Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献

Session details: Salton Award 会议详情:索尔顿奖

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/3255914

C. Clarke

引用次数: 0

Topic-centric Classification of Twitter User's Political Orientation 以话题为中心的Twitter用户政治倾向分类

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767833

Anjie Fang, I. Ounis, P. Habel, C. Macdonald, Nut Limsopatham

{"title":"Topic-centric Classification of Twitter User's Political Orientation","authors":"Anjie Fang, I. Ounis, P. Habel, C. Macdonald, Nut Limsopatham","doi":"10.1145/2766462.2767833","DOIUrl":"https://doi.org/10.1145/2766462.2767833","url":null,"abstract":"In the recent Scottish Independence Referendum (hereafter, IndyRef), Twitter offered a broad platform for people to express their opinions, with millions of IndyRef tweets posted over the campaign period. In this paper, we aim to classify people's voting intentions by the content of their tweets---their short messages communicated on Twitter. By observing tweets related to the IndyRef, we find that people not only discussed the vote, but raised topics related to an independent Scotland including oil reserves, currency, nuclear weapons, and national debt. We show that the views communicated on these topics can inform us of the individuals' voting intentions (\"Yes\"--in favour of Independence vs. \"No\"--Opposed). In particular, we argue that an accurate classifier can be designed by leveraging the differences in the features' usage across different topics related to voting intentions. We demonstrate improvements upon a Naive Bayesian classifier using the topics enrichment method. Our new classifier identifies the closest topic for each unseen tweet, based on those topics identified in the training data. Our experiments show that our Topics-Based Naive Bayesian classifier improves accuracy by 7.8% over the classical Naive Bayesian baseline.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124396325","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: 46

An Aspect-driven Social Media Explorer 方面驱动的社交媒体浏览器

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767864

Nedim Lipka, W. Bruce Croft

引用次数: 0

Inferring Searcher Attention by Jointly Modeling User Interactions and Content Salience 通过用户交互和内容显著性联合建模来推断搜索者的注意力

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767745

Dmitry Lagun, Eugene Agichtein

{"title":"Inferring Searcher Attention by Jointly Modeling User Interactions and Content Salience","authors":"Dmitry Lagun, Eugene Agichtein","doi":"10.1145/2766462.2767745","DOIUrl":"https://doi.org/10.1145/2766462.2767745","url":null,"abstract":"Modeling and predicting user attention is crucial for interpreting search behavior. The numerous applications include quantifying web search satisfaction, estimating search quality, and measuring and predicting online user engagement. While prior research has demonstrated the value of mouse cursor data and other interactions as a rough proxy of user attention, precisely predicting where a user is looking on a page remains a challenge, exacerbated in Web pages beyond the traditional search results. To improve attention prediction on a wider variety of Web pages, we propose a new way of modeling searcher behavior data by connecting the user interactions to the underlying Web page content. Specifically, we propose a principled model for predicting a searcher's gaze position on a page, that we call Mixture of Interactions and Content Salience (MICS). To our knowledge, our model is the first to effectively combine user interaction data, such as mouse cursor and scrolling positions, with the visual prominence, or salience, of the page content elements. Extensive experiments on multiple popular types of Web content demonstrate that the proposed MICS model significantly outperforms previous approaches to searcher gaze prediction that use only the interaction information. Grounding the observed interactions to the underlying page content provides a general and robust approach to user attention modeling, enabling more powerful tool for search behavior interpretation and ultimately search quality improvements.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125836644","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: 28

Location in Search 搜索位置

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2776783

Vanessa Murdock

{"title":"Location in Search","authors":"Vanessa Murdock","doi":"10.1145/2766462.2776783","DOIUrl":"https://doi.org/10.1145/2766462.2776783","url":null,"abstract":"As users turn increasingly to handheld devices to find information, the research community has focused on real-time location signals (GPS signals) to improve search engine effectiveness. Location signals have been investigated for predicting businesses the user will frequent[3], assigning geographic coordinates to media files[1], and to improve mobile search ranking[2]. While the increased focus on real-time user location has produced excellent research, there remains a gap between the capabilities being developed in the research community, and the capabilities being developed by commercial search engines. The core of this discrepancy between the advances in research and advances in industry is understanding the user's location. The vast majority of research on user location assumes that the user's location is known, because the user has provided a GPS signal. For many systems, there is no GPS signal available. The user may choose not enable it, or the system chooses not to prompt the user for the location because doing so degrades the user experience. For these interactions, the system relies on the user's IP address for location information. Further, much of the current research uses public geocoded data such as Foursquare (http://www.foursquare.com visited June 2015), and Twitter (http://www.twitter.com visited June 2015). These data are an incomplete picture of places a user may visit, and are potentially biased in their representation of actual users. The information contained in these data is not the same type of information typically available to a commercial search engine. In this talk we discuss gaps between current research on location, and industry advances in using location signals to improve search results. We focus on user location as one example of a gap between research and development.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125972686","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

Privacy-Preserving IR 2015: When Information Retrieval Meets Privacy and Security 隐私保护IR 2015:当信息检索满足隐私和安全

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767857

G. Yang, I. Soboroff

引用次数: 8

An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation 基于实体分类的累积引文推荐判别混合模型

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767698

Jingang Wang, Dandan Song, Qifan Wang, Zhiwei Zhang, Luo Si, L. Liao, Chin-Yew Lin

{"title":"An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation","authors":"Jingang Wang, Dandan Song, Qifan Wang, Zhiwei Zhang, Luo Si, L. Liao, Chin-Yew Lin","doi":"10.1145/2766462.2767698","DOIUrl":"https://doi.org/10.1145/2766462.2767698","url":null,"abstract":"This paper studies Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to handle unseen entities without annotation. A baseline solution is to build a global entity-unspecific model for all entities regardless of the relationship information among entities, which cannot guarantee to achieve satisfactory result for each entity. In this paper, we propose a novel entity class-dependent discriminative mixture model by introducing a latent entity class layer to model the correlations between entities and latent entity classes. The model can better adjust to different types of entities and achieve better performance when dealing with a broad range of entities. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116089695","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: 15

Adaptive User Engagement Evaluation via Multi-task Learning 基于多任务学习的自适应用户粘性评估

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767785

Hamed Zamani, Pooya Moradi, A. Shakery

引用次数: 7

Cognitive Activity during Web Search 网络搜索过程中的认知活动

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767784

Md. Hedayetul Islam Shovon, N. Nandagopal, J. Du, Vijayalakshmi Ramasamy, Bernadine Cocks

{"title":"Cognitive Activity during Web Search","authors":"Md. Hedayetul Islam Shovon, N. Nandagopal, J. Du, Vijayalakshmi Ramasamy, Bernadine Cocks","doi":"10.1145/2766462.2767784","DOIUrl":"https://doi.org/10.1145/2766462.2767784","url":null,"abstract":"Searching on the Web or Net-surfing is a part of everyday life for many people, but little is known about the brain activity during Web searching. Such knowledge is essential for better understanding of the cognitive demands imposed by the search system and search tasks. The current study contributes to this understanding by constructing brain networks from EEG data using normalized transfer entropy (NTE) during three Web search task stages: query formulation, viewing of a search result list and reading each individual content page. This study further contributes to the connectivity analysis of the constructed brain networks, since it is an advanced quantitative technique which enables the exploration of brain function by distinct and varied brain areas. By using this approach, we identified that the cognitive activities during the three stages of Web searching are different, with various brain areas becoming more active during the three Web search task stages. Of note, query formulation generated higher interaction between cortical regions than viewing a result list or reading a content page. These findings will have implications for the improvement of Web search engines and search interfaces.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130576445","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

Using Sensor Metadata Streams to Identify Topics of Local Events in the City 使用传感器元数据流来识别城市本地事件的主题

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767837

M. Albakour, C. Macdonald, I. Ounis

引用次数: 3