Proceedings of the 2015 International Conference on The Theory of Information Retrieval最新文献

Terms, Topics & Tasks: Enhanced User Modelling for Better Personalization 术语、主题和任务:增强用户建模以实现更好的个性化

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809467

Rishabh Mehrotra, Emine Yilmaz

{"title":"Terms, Topics & Tasks: Enhanced User Modelling for Better Personalization","authors":"Rishabh Mehrotra, Emine Yilmaz","doi":"10.1145/2808194.2809467","DOIUrl":"https://doi.org/10.1145/2808194.2809467","url":null,"abstract":"Given the distinct preferences of different users while using search engines, search personalization has become an important problem in information retrieval. Most approaches to search personalization are based on identifying topics a user may be interested in and personalizing search results based on this information. While topical interests information of users can be highly valuable in personalizing search results and improving user experience, it ignores the fact that two different users that have similar topical interests may still be interested in achieving very different tasks with respect to this topic (e.g. the type of tasks a broker is likely to perform related to finance is likely to be very different than that of a regular investor). Hence, considering user's topical interests jointly with the type of tasks they are likely to be interested in could result in better personalised We present an approach that uses search task information embedded in search logs to represent users by their actions over a task-space as well as over their topical-interest space. In particular, we describe a tensor based approach that represents each user in terms of (i) user's topical interests and (ii) user's search task behaviours in a coupled fashion and use these representations for personalization. Additionally, we also integrate user's historic search behavior in a coupled matrix-tensor factorization framework to learn user representations. Through extensive evaluation via query recommendations and user cohort analysis, we demonstrate the value of considering topic specific task information while developing user models.","PeriodicalId":440325,"journal":{"name":"Proceedings of the 2015 International Conference on The Theory of Information Retrieval","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124266955","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: 26

Towards Less Biased Web Search 走向更少偏见的网络搜索

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809476

Xitong Liu, Hui Fang, Deng Cai

{"title":"Towards Less Biased Web Search","authors":"Xitong Liu, Hui Fang, Deng Cai","doi":"10.1145/2808194.2809476","DOIUrl":"https://doi.org/10.1145/2808194.2809476","url":null,"abstract":"Web search engines now serve as essential assistant to help users make decisions in different aspects. Delivering correct and impartial information is a crucial functionality for search engines as any false information may lead to unwise decision and thus undesirable consequences. Unfortunately, a recent study revealed that Web search engines tend to provide biased information with most results supporting users' beliefs conveyed in queries regardless of the truth. In this paper we propose to alleviate bias in Web search through predicting the topical polarity of documents, which is the overall tendency of one document regarding whether it supports or disapproves the belief in query. By applying the prediction to balance search results, users would receive less biased information and therefore make wiser decision. To achieve this goal, we propose a novel textual segment extraction method to distill and generate document feature representation, and leverage convolution neural network, an effective deep learning approach, to predict topical polarity of documents. We conduct extensive experiments on a set of queries with medical indents and demonstrate that our model performs empirically well on identifying topical polarity with satisfying accuracy. To our best knowledge, our work is the first on investigating the mitigation of bias in Web search and could provide directions on future research.","PeriodicalId":440325,"journal":{"name":"Proceedings of the 2015 International Conference on The Theory of Information Retrieval","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126417341","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

Development and Evaluation of Search Tasks for IIR Experiments using a Cognitive Complexity Framework 基于认知复杂性框架的IIR实验搜索任务开发与评价

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809465

D. Kelly, Jaime Arguello, A. Edwards, Wan-Ching Wu

{"title":"Development and Evaluation of Search Tasks for IIR Experiments using a Cognitive Complexity Framework","authors":"D. Kelly, Jaime Arguello, A. Edwards, Wan-Ching Wu","doi":"10.1145/2808194.2809465","DOIUrl":"https://doi.org/10.1145/2808194.2809465","url":null,"abstract":"One of the most challenging aspects of designing interactive information retrieval (IIR) experiments with users is the development of search tasks. We describe an evaluation of 20 search tasks that were designed for use in IIR experiments and developed using a cognitive complexity framework from educational theory. The search tasks represent five levels of cognitive complexity and four topical domains. The tasks were evaluated in the context of a laboratory IIR experiment with 48 participants. Behavioral and self-report data were used to characterize and understand differences among tasks. Results showed more cognitively complex tasks required significantly more search activity from participants (e.g., more queries, clicks, and time to complete). However, participants did not evaluate more cognitively complex tasks as more difficult and were equally satisfied with their performances across tasks. Our work makes four contributions: (1) it adds to what is known about the relationship among task, search behaviors and user experience; (2) it presents a framework for task creation and evaluation; (3) it provides tasks and questionnaires that can be reused by others and (4) it raises questions about findings and assumptions of many recent studies that only use behavioral signals from search logs as evidence for task difficulty and searcher satisfaction, as many of our results directly contradict these findings.","PeriodicalId":440325,"journal":{"name":"Proceedings of the 2015 International Conference on The Theory of Information Retrieval","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132136604","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: 135

Searching for Twitter Posts by Location 按位置搜索Twitter帖子

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809480

Ariana S. Minot, Andrew Heier, Davis E. King, O. Simek, N. Stanisha

引用次数: 7

Random Walks on the Reputation Graph 声望图上的随机漫步

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809462

Sabir Ribas, B. Ribeiro-Neto, Rodrygo L. T. Santos, E. D. S. E. Silva, A. Ueda, N. Ziviani

{"title":"Random Walks on the Reputation Graph","authors":"Sabir Ribas, B. Ribeiro-Neto, Rodrygo L. T. Santos, E. D. S. E. Silva, A. Ueda, N. Ziviani","doi":"10.1145/2808194.2809462","DOIUrl":"https://doi.org/10.1145/2808194.2809462","url":null,"abstract":"The identification of reputable entities is an important task in business, education, and many other fields. On the other hand, as an arguably subjective, multi-faceted concept, quantifying reputation is challenging. In this paper, instead of relying on a single, precise definition of reputation, we propose to exploit the transference of reputation among entities in order to identify the most reputable ones. To this end, we propose a novel random walk model to infer the reputation of a target set of entities with respect to suitable sources of reputation. We instantiate our model in an academic search setting, by modeling research groups as reputation sources and publication venues as reputation targets. By relying on publishing behavior as a reputation signal, we demonstrate the effectiveness of our model in contrast to standard citation-based approaches for identifying reputable venues as well as researchers in the broad area of computer science. In addition, we demonstrate the robustness of our model to perturbations in the selection of reputation sources. Finally, we show that effective reputation sources can be chosen via the proposed model itself in a semi-automatic fashion.","PeriodicalId":440325,"journal":{"name":"Proceedings of the 2015 International Conference on The Theory of Information Retrieval","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132592551","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: 8

The Probability Ranking Principle is Not Optimal in Adversarial Retrieval Settings 在对抗性检索设置中，概率排序原则不是最优的

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809456

R. Ben-Basat, Moshe Tennenholtz, Oren Kurland

引用次数: 17

Learning to Reinforce Search Effectiveness 学习加强搜索效率

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809468

Jiyun Luo, Xuchu Dong, G. Yang

引用次数: 12

On Microblog Dimensionality and Informativeness: Exploiting Microblogs' Structure and Dimensions for Ad-Hoc Retrieval 微博的维数和信息量:利用微博的结构和维数进行自组织检索

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809466

Jesus A. Rodriguez Perez, J. Jose

{"title":"On Microblog Dimensionality and Informativeness: Exploiting Microblogs' Structure and Dimensions for Ad-Hoc Retrieval","authors":"Jesus A. Rodriguez Perez, J. Jose","doi":"10.1145/2808194.2809466","DOIUrl":"https://doi.org/10.1145/2808194.2809466","url":null,"abstract":"In recent years, microblog services such as Twitter have gained increasing popularity, leading to active research on how to effectively exploit its content. Microblog documents such as tweets differ in morphology with respect to more traditional documents such as web pages. Particularly, tweets are considerably shorter (140 characters) than web documents and contain contextual tags regarding the topic (hashtags), intended audience (mentions) of the document as well as links to external content(URLs). Traditional and state of the art retrieval models perform rather poorly in capturing the relevance of tweets, since they have been designed under very different conditions. In this work, we define a microblog document as a high-dimensional entity and study the structural differences between those documents deemed relevant and those non-relevant. Secondly we experiment with enhancing the behaviour of the best observed performing retrieval model by means of a re-ranking approach that accounts for the relative differences in these dimensions amongst tweets. Additionally we study the interactions between the different dimensions in terms of their order within the documents by modelling relevant and non-relevant tweets as state machines. These state machines are then utilised to produce scores which in turn are used for re-ranking. Our evaluation results show statistically significant improvements over the baseline in terms of precision at different cut-off points for both approaches. These results confirm that the relative presence of the different dimensions within a document and their ordering are connected with the relevance of microblogs.","PeriodicalId":440325,"journal":{"name":"Proceedings of the 2015 International Conference on The Theory of Information Retrieval","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124673189","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: 6

An Initial Analytical Exploration of Retrievability 可追偿性的初步分析探讨

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809495

Aldo Lipani, M. Lupu, Akiko Aizawa, A. Hanbury

引用次数: 6

On Divergence Measures and Static Index Pruning 散度测度与静态指数修剪

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI: 10.1145/2808194.2809472

Ruey-Cheng Chen, Chia-Jung Lee, W. Bruce Croft

引用次数: 6