Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval最新文献

ViewSer: enabling large-scale remote user studies of web search examination and interaction ViewSer:允许大规模远程用户研究网络搜索、检查和交互

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2009967

Dmitry Lagun, Eugene Agichtein

{"title":"ViewSer: enabling large-scale remote user studies of web search examination and interaction","authors":"Dmitry Lagun, Eugene Agichtein","doi":"10.1145/2009916.2009967","DOIUrl":"https://doi.org/10.1145/2009916.2009967","url":null,"abstract":"Web search behaviour studies, including eye-tracking studies of search result examination, have resulted in numerous insights to improve search result quality and presentation. Yet, eye tracking studies have been restricted in scale, due to the expense and the effort required. Furthermore, as the reach of the Web expands, it becomes increasingly important to understand how searchers around the world see and interact with the search results. To address both challenges, we introduce ViewSer, a novel methodology for performing web search examination studies remotely, at scale, and without requiring eye-tracking equipment. ViewSer operates by automatically modifying the appearance of a search engine result page, to clearly show one search result at a time as if through a \"viewport\", while partially blurring the rest and allowing the participant to move the viewport naturally with a computer mouse or trackpad. Remarkably, the resulting result viewing and clickthrough patterns agree closely with unrestricted viewing of results, as measured by eye-tracking equipment, validated by a study with over 100 participants. We also explore applications of ViewSer to practical search tasks, such as analyzing the search result summary (snip- pet) attractiveness, result re-ranking, and evaluating snippet quality. These experiments could have only be done previously by tracking the eye movements for a small number of subjects in the lab. In contrast, our study was performed with over 100 participants, allowing us to reproduce and extend previous findings, establishing ViewSer as a valuable tool for large-scale search behavior experiments.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127512211","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: 60

What-you-retrieve-is-what-you-see: a preliminary cyber-physical search engine 你所检索的就是你所看到的:一个初步的网络物理搜索引擎

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010156

L. Shou, Ke Chen, Gang Chen, Chao Zhang, Yi Ma, X. Zhang

引用次数: 3

Competition-based user expertise score estimation 基于竞争的用户经验评分估计

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2009975

Jing Liu, Young-In Song, Chin-Yew Lin

{"title":"Competition-based user expertise score estimation","authors":"Jing Liu, Young-In Song, Chin-Yew Lin","doi":"10.1145/2009916.2009975","DOIUrl":"https://doi.org/10.1145/2009916.2009975","url":null,"abstract":"In this paper, we consider the problem of estimating the relative expertise score of users in community question and answering services (CQA). Previous approaches typically only utilize the explicit question answering relationship between askers and an-swerers and apply link analysis to address this problem. The im-plicit pairwise comparison between two users that is implied in the best answer selection is ignored. Given a question and answering thread, it's likely that the expertise score of the best answerer is higher than the asker's and all other non-best answerers'. The goal of this paper is to explore such pairwise comparisons inferred from best answer selections to estimate the relative expertise scores of users. Formally, we treat each pairwise comparison between two users as a two-player competition with one winner and one loser. Two competition models are proposed to estimate user expertise from pairwise comparisons. Using the NTCIR-8 CQA task data with 3 million questions and introducing answer quality prediction based evaluation metrics, the experimental results show that the pairwise comparison based competition model significantly outperforms link analysis based approaches (PageRank and HITS) and pointwise approaches (number of best answers and best answer ratio) for estimating the expertise of active users. Furthermore, it's shown that pairwise comparison based competi-tion models have better discriminative power than other methods. It's also found that answer quality (best answer) is an important factor to estimate user expertise.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125532952","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: 83

Active learning to maximize accuracy vs. effort in interactive information retrieval 主动学习，最大限度地提高准确性与努力在交互式信息检索

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2009939

Aibo Tian, Matthew Lease

引用次数: 27

Intent-oriented diversity in recommender systems 推荐系统中意向导向的多样性

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010124

S. Vargas, P. Castells, D. Vallet

引用次数: 73

Enhancing mobile search using web search log data 使用web搜索日志数据增强移动搜索

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010119

Yoshiyuki Inagaki, Jiang Bian, Yi Chang, Motoko Maki

引用次数: 1

Detecting success in mobile search from interaction 从交互中检测移动搜索的成功

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010133

Qi Guo, Shuai Yuan, Eugene Agichtein

引用次数: 23

Region-based landmark discovery by crowdsourcing geo-referenced photos 通过众包地理参考照片发现基于区域的地标

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010089

Yen-Ta Huang, A. Cheng, Liang-Chi Hsieh, Winston H. Hsu, Kuo-Wei Chang

引用次数: 4

Parameterized concept weighting in verbose queries 详细查询中的参数化概念权重

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2009998

Michael Bendersky, Donald Metzler, W. Bruce Croft

引用次数: 117

A comparison of time-aware ranking methods 时间感知排序方法的比较

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010147

Nattiya Kanhabua, K. Nørvåg

引用次数: 20