Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval最新文献

{"title":"Browse with a social web directory","authors":"H. Huang, Yunjun Gao, Lu Chen, Rui Li, K. Chiew, Qinming He","doi":"10.1145/2484028.2484141","DOIUrl":"https://doi.org/10.1145/2484028.2484141","url":null,"abstract":"Browse with either web directories or social bookmarks is an important complementation to search by keywords in web information retrieval. To improve users' browse experiences and facilitate the web directory construction, in this paper, we propose a novel browse system called Social Web Directory (SWD for short) by integrating web directories and social bookmarks. In SWD, (1) web pages are automatically categorized to a hierarchical structure to be retrieved efficiently, and (2) the popular web pages, hottest tags, and expert users in each category are ranked to help users find information more conveniently. Extensive experimental results demonstrate the effectiveness of our SWD system.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122223130","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}

{"title":"Informational friend recommendation in social media","authors":"Shengxian Wan, Yanyan Lan, J. Guo, Chaosheng Fan, Xueqi Cheng","doi":"10.1145/2484028.2484179","DOIUrl":"https://doi.org/10.1145/2484028.2484179","url":null,"abstract":"It is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common needs (i.e. social need and informational need) that is to keep in touch with their friends in the real world and to have access to information they are interested in. Traditional friend recommendation methods in social media mainly focus on a user's social need, but seldom address their informational need (i.e. suggesting friends that can provide information one may be interested in but have not been able to obtain so far). In this paper, we propose to recommend friends according to the informational utility, which stands for the degree to which a friend satisfies the target user's unfulfilled informational need, called informational friend recommendation. In order to capture users' informational need, we view a post in social media as an item and utilize collaborative filtering techniques to predict the rating for each post. The candidate friends are then ranked according to their informational utility for recommendation. In addition, we also show how to further consider diversity in such recommendations. Experiments on benchmark datasets demonstrate that our approach can significantly outperform the traditional friend recommendation methods under informational evaluation measures.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131202271","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}

{"title":"Explicit feedback in local search tasks","authors":"Dmitry Lagun, Avneesh Sud, Ryen W. White, P. Bailey, Georg Buscher","doi":"10.1145/2484028.2484123","DOIUrl":"https://doi.org/10.1145/2484028.2484123","url":null,"abstract":"Modern search engines make extensive use of people's contextual information to finesse result rankings. Using a searcher's location provides an especially strong signal for adjusting results for certain classes of queries where people may have clear preference for local results, without explicitly specifying the location in the query direct-ly. However, if the location estimate is inaccurate or searchers want to obtain many results from a particular location, they have limited control on the location focus in the search results returned. In this paper we describe a user study that examines the effect of offering searchers more control over how local preferences are gathered and used. We studied providing users with functionality to offer explicit relevance feedback (ERF) adjacent to results automatically identi-fied as location-dependent (i.e., more from this location). They can use this functionality to indicate whether they are interested in a particular search result and desire more results from that result's location. We compared the ERF system against a baseline (NoERF) that used the same underlying mechanisms to retrieve and rank results, but did not offer ERF support. User performance was as-sessed across 12 experimental participants over 12 location-sensitive topics, in a fully counter-balanced design. We found that participants interacted with ERF frequently, and there were signs that ERF has the potential to improve success rates and lead to more efficient searching for location-sensitive search tasks than NoERF.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015235","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}

{"title":"Learning to name faces: a multimodal learning scheme for search-based face annotation","authors":"Dayong Wang, S. Hoi, Pengcheng Wu, Jianke Zhu, Ying He, C. Miao","doi":"10.1145/2484028.2484040","DOIUrl":"https://doi.org/10.1145/2484028.2484040","url":null,"abstract":"Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, this paper proposes a novel framework of Learning to Name Faces (L2NF) -- a unified multimodal learning approach for search-based face annotation, which consists of the following major components: (i) we enhance the weak labels of top-ranked similar images by exploiting the \"label smoothness\" assumption; (ii) we construct the multimodal representations of a facial image by extracting different types of features; (iii) we optimize the distance measure for each type of features using distance metric learning techniques; and finally (iv) we learn the optimal combination of multiple modalities for annotation through a learning to rank scheme. We conduct a set of extensive empirical studies on two real-world facial image databases, in which encouraging results show that the proposed algorithms significantly boost the naming accuracy of search-based face annotation task.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134090909","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}

{"title":"ThemeStreams: visualizing the stream of themes discussed in politics","authors":"O. D. Rooij, Daan Odijk, M. de Rijke","doi":"10.1145/2484028.2484215","DOIUrl":"https://doi.org/10.1145/2484028.2484215","url":null,"abstract":"The political landscape is fluid. Discussions are always ongoing and new \"hot topics\" continue to appear in the headlines. But what made people start talking about that topic? And who started it? Because of the speed at which discussions sometimes take place this can be difficult to track down. We describe ThemeStreams: a demonstrator that maps political discussions to themes and influencers and illustrate how this mapping is used in an interactive visualization that shows us which themes are being discussed, and that helps us answer the question \"Who put this issue on the map?\" in streams of political data.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133881364","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}

{"title":"Designing search usability","authors":"Tony Russell-Rose","doi":"10.1007/978-3-319-12979-2_10","DOIUrl":"https://doi.org/10.1007/978-3-319-12979-2_10","url":null,"abstract":"","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133906244","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}

{"title":"Effective approaches to retrieving and using expertise in social media","authors":"Reyyan Yeniterzi","doi":"10.1145/2484028.2484230","DOIUrl":"https://doi.org/10.1145/2484028.2484230","url":null,"abstract":"Expert retrieval has been widely studied especially after the introduction of Expert Finding task in the TREC's Enterprise Track in 2005 [3]. This track provided two different test collections crawled from two organizations' public-facing websites and internal emails which led to the development of many state-of-the-art algorithms on expert retrieval [1]. Until recently, these datasets were considered good representatives of the information resources available within enterprise. However, the recent growth of social media also influenced the work environment, and social media became a common communication and collaboration tool within organizations. According to a recent survey by McKinsey Global Institute [2], 29% of the companies use at least one social media tool for matching their employees to tasks, and 26% of them assess their employees' performance by using social media. This shows that intra-organizational social media became an important resource to identify expertise within organizations. In recent years, in addition to the intra-organizational social media, public social media tools like Twitter, Facebook, LinkedIn also became common environments for searching expertise. These tools provide an opportunity for their users to show their specific skills to the world which motivates recruiters to look for talented job candidates on social media, or writers and reporters to find experts for consulting on specific topics they are working on. With these motivations in mind, in this work we propose to develop expert retrieval algorithms for intra-organizational and public social media tools. Social media datasets have both challenges and advantages. In terms of challenges, they do not always contain context on one specific domain, instead one social media tool may contain discussions on technical stuff, hobbies or news concurrently. They may also contain spam posts or advertisements. Compared to well-edited enterprise documents, they are much more informal in language. Furthermore, depending on the social media platform, they may have limits on the number of characters used in posts. Even though they include the challenges stated above, they also bring some unique authority signals, such as votes, comments, follower/following information, which can be useful in estimating expertise. Furthermore, compared to previously used enterprise documents, social media provides clear associations between documents and candidates in the context of authorship information. In this work, we propose to develop expert retrieval approaches which will handle these challenges while making use of the advantages. Expert retrieval is a very useful application by itself; furthermore, it can be a step towards improving other social media applications. Social media is different than other web based tools mainly because it is dependent on its users. In social media, users are not just content consumers, but they are also the primary and sometimes the only content creators","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127693238","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}

{"title":"Answering natural language queries over linked data graphs: a distributional semantics approach","authors":"A. Freitas, Fabrício F. de Faria, Seán O'Riain, E. Curry","doi":"10.1145/2484028.2484209","DOIUrl":"https://doi.org/10.1145/2484028.2484209","url":null,"abstract":"This paper demonstrates Treo, a natural language query mechanism for Linked Data graphs. The approach uses a distributional semantic vector space model to semantically match user query terms with data, supporting vocabulary-independent (or schema-agnostic) queries over structured data.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127934250","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}

{"title":"A query and patient understanding framework for medical records search","authors":"Nut Limsopatham","doi":"10.1145/2484028.2484228","DOIUrl":"https://doi.org/10.1145/2484028.2484228","url":null,"abstract":"Electronic medical records (EMRs) are being increasingly used worldwide to facilitate improved healthcare services [2,3]. They describe the clinical decision process relating to a patient, detailing the observed symptoms, the conducted diagnostic tests, the identified diagnoses and the prescribed treatments. However, medical records search is challenging, due to the implicit knowledge inherent within the medical records - such knowledge may be known by medical practitioners, but hidden to an information retrieval (IR) system [3]. For instance, the mention of a treatment such as a drug may indicate to a practitioner that a particular diagnosis has been made even if this was not explicitly mentioned in the patient's EMRs. Moreover, the fact that a symptom has not been observed by a clinician may rule out some specific diagnoses. Our work focuses on searching EMRs to identify patients with medical histories relevant to the medical condition(s) stated in a query. The resulting system can be beneficial to healthcare providers, administrators, and researchers who may wish to analyse the effectiveness of a particular medical procedure to combat a specific disease [2,4]. During retrieval, a healthcare provider may indicate a number of inclusion criteria to describe the type of patients of interest. For example, the used criteria may include personal profiles (e.g. age and gender) or some specific medical symptoms and tests, allowing to identify patients that have EMRs matching the criteria. To attain effective retrieval performance, we hypothesise that, in such a medical IR system, both the information needs and patients should be modelled based on how the medical process is developed. Specifically, our thesis states that since the medical decision process typically encompasses four aspects (symptom, diagnostic test, diagnosis, and treatment), a medical search system should take into account these aspects and apply inferences to recover possible implicit knowledge. We postulate that considering these aspects and their derived implicit knowledge at different levels of the retrieval process (namely, sentence, record, and inter-record level) enhances the retrieval performance. Indeed, we propose to build a query and patient understanding framework that can gain insights from EMRs and queries, by modelling and reasoning during retrieval in terms of the four aforementioned aspects (symptom, diagnostic test, diagnosis, and treatment) at three different levels of the retrieval process.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129079654","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}

{"title":"TweetMogaz: a news portal of tweets","authors":"Walid Magdy","doi":"10.1145/2484028.2484212","DOIUrl":"https://doi.org/10.1145/2484028.2484212","url":null,"abstract":"Twitter is currently one of the largest social hubs for users to spread and discuss news. For most of the top news stories happening, there are corresponding discussions on social media. In this demonstration TweetMogaz is presented, which is a platform for microblog search and filtering. It creates a real-time comprehensive report about what people discuss and share around news happening in certain regions. TweetMogaz reports the most popular tweets, jokes, videos, images, and news articles that people share about top news stories. Moreover, it allows users to search for specific topics. A scalable automatic technique for microblog filtering is used to obtain relevant tweets to a certain news category in a region. TweetMogaz.com demonstrates the effectiveness of our filtering technique for reporting public response toward news in different Arabic regions including Egypt and Syria in real-time.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129305104","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}