Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第3页

Analyzing User's Sequential Behavior in Query Auto-Completion via Markov Processes 基于马尔可夫过程的查询自动完成中用户顺序行为分析

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767723

Liangda Li, Hongbo Deng, Anlei Dong, Yi Chang, H. Zha, R. Baeza-Yates

{"title":"Analyzing User's Sequential Behavior in Query Auto-Completion via Markov Processes","authors":"Liangda Li, Hongbo Deng, Anlei Dong, Yi Chang, H. Zha, R. Baeza-Yates","doi":"10.1145/2766462.2767723","DOIUrl":"https://doi.org/10.1145/2766462.2767723","url":null,"abstract":"Query auto-completion (QAC) plays an important role in assisting users typing less while submitting a query. The QAC engine generally offers a list of suggested queries that start with a user's input as a prefix, and the list of suggestions is changed to match the updated input after the user types each keystroke. Therefore rich user interactions can be observed along with each keystroke until a user clicks a suggestion or types the entire query manually. It becomes increasingly important to analyze and understand users' interactions with the QAC engine, to improve its performance. Existing works on QAC either ignored users' interaction data, or assumed that their interactions at each keystroke are independent from others. Our paper pays high attention to users' sequential interactions with a QAC engine in and across QAC sessions, rather than users' interactions at each keystroke of each QAC session separately. Analyzing the dependencies in users' sequential interactions improves our understanding of the following three questions: 1) how is a user's skipping/viewing move at the current keystroke influenced by that at the previous keystroke? 2) how to improve search engines' query suggestions at short keystrokes based on those at latter long keystrokes? and 3) facing a targeted query shown in the suggestion list, why does a user decide to continue typing rather than click the intended suggestion? We propose a probabilistic model that addresses those three questions in a unified way, and illustrate how the model determines users' final click decisions. By comparing with state-of-the-art methods, our proposed model does suggest queries that better satisfy users' intents.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 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":"131254189","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: 30

Towards Understanding the Impact of Length in Web Search Result Summaries over a Speech-only Communication Channel 在纯语音通信通道上理解长度对网络搜索结果摘要的影响

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767826

Johanne R. Trippas, Damiano Spina, M. Sanderson, L. Cavedon

引用次数: 25

Modeling Multi-query Retrieval Tasks Using Density Matrix Transformation 基于密度矩阵变换的多查询检索任务建模

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767819

Qiuchi Li, Jingfei Li, Peng Zhang, D. Song

{"title":"Modeling Multi-query Retrieval Tasks Using Density Matrix Transformation","authors":"Qiuchi Li, Jingfei Li, Peng Zhang, D. Song","doi":"10.1145/2766462.2767819","DOIUrl":"https://doi.org/10.1145/2766462.2767819","url":null,"abstract":"The quantum probabilistic framework has recently been applied to Information Retrieval (IR). A representative is the Quantum Language Model (QLM), which is developed for the ad-hoc retrieval with single queries and has achieved significant improvements over traditional language models. In QLM, a density matrix, defined on the quantum probabilistic space, is estimated as a representation of user's search intention with respect to a specific query. However, QLM is unable to capture the dynamics of user's information need in query history. This limitation restricts its further application on the dynamic search tasks, e.g., session search. In this paper, we propose a Session-based Quantum Language Model (SQLM) that deals with multi-query session search task. In SQLM, a transformation model of density matrices is proposed to model the evolution of user's information need in response to the user's interaction with search engine, by incorporating features extracted from both positive feedback (clicked documents) and negative feedback (skipped documents). Extensive experiments conducted on TREC 2013 and 2014 session track data demonstrate the effectiveness of SQLM in comparison with the classic QLM.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"16 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":"114316445","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

Time Pressure in Information Search 信息搜索中的时间压力

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767851

Anita Crescenzi

引用次数: 0

Relevance Scores for Triples from Type-Like Relations 类类型关系中三元组的相关性评分

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767734

H. Bast, Björn Buchhold, Elmar Haussmann

{"title":"Relevance Scores for Triples from Type-Like Relations","authors":"H. Bast, Björn Buchhold, Elmar Haussmann","doi":"10.1145/2766462.2767734","DOIUrl":"https://doi.org/10.1145/2766462.2767734","url":null,"abstract":"We compute and evaluate relevance scores for knowledge-base triples from type-like relations. Such a score measures the degree to which an entity \"belongs\" to a type. For example, Quentin Tarantino has various professions, including Film Director, Screenwriter, and Actor. The first two would get a high score in our setting, because those are his main professions. The third would get a low score, because he mostly had cameo appearances in his own movies. Such scores are essential in the ranking for entity queries, e.g. \"American actors\" or \"Quentin Tarantino professions\". These scores are different from scores for \"correctness\" or \"accuracy\" (all three professions above are correct and accurate). We propose a variety of algorithms to compute these scores. For our evaluation we designed a new benchmark, which includes a ground truth based on about 14K human judgments obtained via crowdsourcing. Inter-judge agreement is slightly over 90%. Existing approaches from the literature give results far from the optimum. Our best algorithms achieve an agreement of about 80% with the ground truth.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 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":"116310516","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: 40

Personalizing Search on Shared Devices 在共享设备上个性化搜索

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767736

Ryen W. White, Ahmed Hassan Awadallah

{"title":"Personalizing Search on Shared Devices","authors":"Ryen W. White, Ahmed Hassan Awadallah","doi":"10.1145/2766462.2767736","DOIUrl":"https://doi.org/10.1145/2766462.2767736","url":null,"abstract":"Search personalization tailors the search experience to individual searchers. To do this, search engines construct interest models comprising signals from observed behavior associated with ma-chines, often via Web browser cookies or other user identifiers. However, shared device usage is common, meaning that the activities of multiple searchers may be interwoven in the interest models generated. Recent research on activity attribution has led to methods to automatically disentangle the histories of multiple searchers and correctly ascribe newly-observed search activity to the correct per-son. Building on this, we introduce attribution-based personalization (ABP), a procedure that extends traditional personalization to target individual searchers on shared devices. Activity attribution may improve personalization, but its benefits are not yet fully understood. We present an oracle study (with perfect knowledge of which searchers perform each action on each machine) to under-stand the effectiveness of ABP in predicting searchers' future interests. We utilize a large Web search log dataset containing both per-son identifiers and machine identifiers to quantify the gain in personalization performance from ABP, identify the circumstances under which ABP is most effective, and develop a classifier to determine when to apply it that yields sizable gains in personalization performance. ABP allows search providers to personalize experiences for individuals rather than targeting all users of a device collectively.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"75 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":"116415460","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: 5

Finding Money in the Haystack: Information Retrieval at Bloomberg 在干草堆里找钱:彭博社的信息检索

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2776782

Jonathan J. Dorando, Konstantine Arkoudas, P. Vasa, Gary Kazantsev, Gideon Mann

引用次数: 0

Different Users, Different Opinions: Predicting Search Satisfaction with Mouse Movement Information 不同的用户，不同的意见:用鼠标移动信息预测搜索满意度

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767721

Yiqun Liu, Ye Chen, Jinhui Tang, Jiashen Sun, Min Zhang, Shaoping Ma, Xuan Zhu

{"title":"Different Users, Different Opinions: Predicting Search Satisfaction with Mouse Movement Information","authors":"Yiqun Liu, Ye Chen, Jinhui Tang, Jiashen Sun, Min Zhang, Shaoping Ma, Xuan Zhu","doi":"10.1145/2766462.2767721","DOIUrl":"https://doi.org/10.1145/2766462.2767721","url":null,"abstract":"Satisfaction prediction is one of the prime concerns in search performance evaluation. It is a non-trivial task for two major reasons: (1) The definition of satisfaction is rather subjective and different users may have different opinions in satisfaction judgement. (2) Most existing studies on satisfaction prediction mainly rely on users' click-through or query reformulation behaviors but there are many sessions without such kind of interactions. To shed light on these research questions, we construct an experimental search engine that could collect users' satisfaction feedback as well as mouse click-through/movement data. Different from existing studies, we compare for the first time search users' and external assessors' opinions on satisfaction. We find that search users pay more attention to the utility of results while external assessors emphasize on the efforts spent in search sessions. Inspired by recent studies in predicting result relevance based on mouse movement patterns (namely motifs), we propose to estimate the utilities of search results and the efforts in search sessions with motifs extracted from mouse movement data on search result pages (SERPs). Besides the existing frequency-based motif selection method, two novel selection strategies (distance-based and distribution-based) are also adopted to extract high quality motifs for satisfaction prediction. Experimental results on over 1,000 user sessions show that the proposed strategies outperform existing methods and also have promising generalization capability for different users and queries.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 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":"125901368","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: 71

Reachability based Ranking in Interactive Image Retrieval 基于可达性排序的交互式图像检索

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767777

Jiyi Li

引用次数: 1

Using Term Location Information to Enhance Probabilistic Information Retrieval 利用词位信息增强概率信息检索

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767827

Baiyan Liu, X. An, Xiangji Huang

引用次数: 25