Proceedings of the 22nd ACM international conference on Information & Knowledge Management最新文献_第10页

Supporting exploratory people search: a study of factor transparency and user control 支持探索性人员搜索:因素透明度和用户控制的研究

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505684

Shuguang Han, Daqing He, Jiepu Jiang, Zhen Yue

{"title":"Supporting exploratory people search: a study of factor transparency and user control","authors":"Shuguang Han, Daqing He, Jiepu Jiang, Zhen Yue","doi":"10.1145/2505515.2505684","DOIUrl":"https://doi.org/10.1145/2505515.2505684","url":null,"abstract":"People search is an active research topic in recent years. Related works includes expert finding, collaborator recommendation, link prediction and social matching. However, the diverse objectives and exploratory nature of those tasks make it difficult to develop a flexible method for people search that works for every task. In this project, we developed PeopleExplorer, an interactive people search system to support exploratory search tasks when looking for people. In the system, users could specify their task objectives by selecting and adjusting key criteria. Three criteria were considered: the content relevance, the candidate authoritativeness and the social similarity between the user and the candidates. This project represents a first attempt to add transparency to exploratory people search, and to give users full control over the search process. The system was evaluated through an experiment with 24 participants undertaking four different tasks. The results show that with comparable time and effort, users of our system performed significantly better in their people search tasks than those using the baseline system. Users of our system also exhibited many unique behaviors in query reformulation and candidate selection. We found that users' general perceptions about three criteria varied during different tasks, which confirms our assumptions regarding modeling task difference and user variance in people search systems.","PeriodicalId":20528,"journal":{"name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","volume":"2012 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86356299","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: 29

User intent and assessor disagreement in web search evaluation 网络搜索评价中的用户意图与评估者分歧

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505716

G. Kazai, Emine Yilmaz, Nick Craswell, S. Tahaghoghi

{"title":"User intent and assessor disagreement in web search evaluation","authors":"G. Kazai, Emine Yilmaz, Nick Craswell, S. Tahaghoghi","doi":"10.1145/2505515.2505716","DOIUrl":"https://doi.org/10.1145/2505515.2505716","url":null,"abstract":"Preference based methods for collecting relevance data for information retrieval (IR) evaluation have been shown to lead to better inter-assessor agreement than the traditional method of judging individual documents. However, little is known as to why preference judging reduces assessor disagreement and whether better agreement among assessors also means better agreement with user satisfaction, as signaled by user clicks. In this paper, we examine the relationship between assessor disagreement and various click based measures, such as click preference strength and user intent similarity, for judgments collected from editorial judges and crowd workers using single absolute, pairwise absolute and pairwise preference based judging methods. We find that trained judges are significantly more likely to agree with each other and with users than crowd workers, but inter-assessor agreement does not mean agreement with users. Switching to a pairwise judging mode improves crowdsourcing quality close to that of trained judges. We also find a relationship between intent similarity and assessor-user agreement, where the nature of the relationship changes across judging modes. Overall, our findings suggest that the awareness of different possible intents, enabled by pairwise judging, is a key reason of the improved agreement, and a crucial requirement when crowdsourcing relevance data.","PeriodicalId":20528,"journal":{"name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82123574","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

Interest mining from user tweets 从用户tweets中挖掘兴趣

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2507883

Thuy Vu, V. Perez

引用次数: 15

Intelligent SSD: a turbo for big data mining 智能SSD:大数据挖掘的涡轮增压

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2507847

Duck-Ho Bae, Jin-Hyung Kim, Yong-Yeon Jo, Sang-Wook Kim, Hyun-Kyo Oh, Chanik Park

引用次数: 44

Mining entity attribute synonyms via compact clustering 通过紧凑聚类挖掘实体属性同义词

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505608

Yanen Li, B. Hsu, ChengXiang Zhai, Kuansan Wang

引用次数: 12

TellMyRelevance!: predicting the relevance of web search results from cursor interactions TellMyRelevance !:根据游标交互预测网络搜索结果的相关性

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505703

Maximilian Speicher, A. Both, M. Gaedke

引用次数: 22

Automated probabilistic modeling for relational data 关系数据的自动概率建模

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2507828

Sameer Singh, T. Graepel

引用次数: 11

An empirical study of top-n recommendation for venture finance 风险融资top-n推荐的实证研究

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2507882

T. Stone, Weinan Zhang, Xiaoxue Zhao

引用次数: 20

Improving pseudo-relevance feedback via tweet selection 通过tweet选择改善伪相关性反馈

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505701

Taiki Miyanishi, Kazuhiro Seki, K. Uehara

{"title":"Improving pseudo-relevance feedback via tweet selection","authors":"Taiki Miyanishi, Kazuhiro Seki, K. Uehara","doi":"10.1145/2505515.2505701","DOIUrl":"https://doi.org/10.1145/2505515.2505701","url":null,"abstract":"Query expansion methods using pseudo-relevance feedback have been shown effective for microblog search because they can solve vocabulary mismatch problems often seen in searching short documents such as Twitter messages (tweets), which are limited to 140 characters. Pseudo-relevance feedback assumes that the top ranked documents in the initial search results are relevant and that they contain topic-related words appropriate for relevance feedback. However, those assumptions do not always hold in reality because the initial search results often contain many irrelevant documents. In such a case, only a few of the suggested expansion words may be useful with many others being useless or even harmful. To overcome the limitation of pseudo-relevance feedback for microblog search, we propose a novel query expansion method based on two-stage relevance feedback that models search interests by manual tweet selection and integration of lexical and temporal evidence into its relevance model. Our experiments using a corpus of microblog data (the Tweets2011 corpus) demonstrate that the proposed two-stage relevance feedback approaches considerably improve search result relevance over almost all topics.","PeriodicalId":20528,"journal":{"name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80545887","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: 57

Building user profiles from topic models for personalised search 从主题模型构建用户配置文件，用于个性化搜索

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI: 10.1145/2505515.2505642

Morgan Harvey, F. Crestani, Mark James Carman

引用次数: 97