Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval最新文献_第5页

Exploratory Search Pipes with Scoped Facets 具有作用域facet的探索性搜索管道

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344247

Tim Gollub, Leon Hutans, Tanveer Al Jami, Benno Stein

引用次数: 6

Investigating the Reliability of Click Models 点击模型的可靠性研究

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344242

Jiaxin Mao, Zhumin Chu, Yiqun Liu, Min Zhang, Shaoping Ma

引用次数: 8

DC3 -- A Diagnostic Case Challenge Collection for Clinical Decision Support DC3——用于临床决策支持的诊断病例挑战集

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-08-22 DOI: 10.1145/3341981.3344239

Carsten Eickhoff, Floran Gmehlin, Anu Patel, Jocelyn Boullier, H. Fraser

{"title":"DC3 -- A Diagnostic Case Challenge Collection for Clinical Decision Support","authors":"Carsten Eickhoff, Floran Gmehlin, Anu Patel, Jocelyn Boullier, H. Fraser","doi":"10.1145/3341981.3344239","DOIUrl":"https://doi.org/10.1145/3341981.3344239","url":null,"abstract":"In clinical care, obtaining a correct diagnosis is the first step towards successful treatment and, ultimately, recovery. Depending on the complexity of the case, the diagnostic phase can be lengthy and ridden with errors and delays. Such errors have a high likelihood to cause patients severe harm or even lead to their death and are estimated to cost the U.S. healthcare system several hundred billion dollars each year. To avoid diagnostic errors, physicians increasingly rely on diagnostic decision support systems drawing from heuristics, historic cases, textbooks, clinical guidelines and scholarly biomedical literature. The evaluation of such systems, however, is often conducted in an ad-hoc fashion, using non-transparent methodology, and proprietary data. This paper presents DC3, a collection of 31 extremely difficult diagnostic case challenges, manually compiled and solved by clinical experts. For each case, we present a number of temporally ordered physician-generated observations alongside the eventually confirmed true diagnosis. We additionally provide inferred dense relevance judgments for these cases among the PubMed collection of 27 million scholarly biomedical articles.","PeriodicalId":173154,"journal":{"name":"Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114165432","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: 1

Neural Document Expansion with User Feedback 基于用户反馈的神经文档扩展

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-08-08 DOI: 10.1145/3341981.3344213

Yue Yin, Chenyan Xiong, Cheng Luo, Zhiyuan Liu

引用次数: 0

Unsupervised Context Retrieval for Long-tail Entities 长尾实体的无监督上下文检索

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-08-05 DOI: 10.1145/3341981.3344244

Darío Garigliotti, M. Albakour, Miguel Martinez, K. Balog

引用次数: 1

Category-Aware Location Embedding for Point-of-Interest Recommendation 兴趣点推荐的类别感知位置嵌入

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-07-31 DOI: 10.1145/3341981.3344240 10.1145/3341981.3344240 10.1145/3341981.3344240

Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, M. Afsharchi, F. Crestani

{"title":"Category-Aware Location Embedding for Point-of-Interest Recommendation","authors":"Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, M. Afsharchi, F. Crestani","doi":"10.1145/3341981.3344240 10.1145/3341981.3344240 10.1145/3341981.3344240","DOIUrl":"https://doi.org/10.1145/3341981.3344240 10.1145/3341981.3344240 10.1145/3341981.3344240","url":null,"abstract":"Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods.","PeriodicalId":173154,"journal":{"name":"Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122727150","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

Modelling Dynamic Interactions Between Relevance Dimensions 相关维度之间的动态交互建模

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-07-25 DOI: 10.1145/3341981.3344233

Sagar Uprety, S. Dehdashti, Lauren Fell, P. Bruza, D. Song

引用次数: 3

Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks 使用深度神经网络学习多变量分组评分函数

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2018-11-11 DOI: 10.1145/3341981.3344218

Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork

{"title":"Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks","authors":"Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork","doi":"10.1145/3341981.3344218","DOIUrl":"https://doi.org/10.1145/3341981.3344218","url":null,"abstract":"While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire input document list. This difference leads to the notion of relative relevance between documents in ranking. The majority of the existing learning-to-rank algorithms model such relativity at the loss level using pairwise or listwise loss functions. However, they are restricted to univariate scoring functions, i.e., the relevance score of a document is computed based on the document itself, regardless of other documents in the list. To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list. We refer to this framework as GSFs---groupwise scoring functions. We learn GSFs with a deep neural network architecture, and demonstrate that several representative learning-to-rank algorithms can be modeled as special cases in our framework. We conduct evaluation using click logs from one of the largest commercial email search engines, as well as a public benchmark dataset. In both cases, GSFs lead to significant performance improvements, especially in the presence of sparse textual features.","PeriodicalId":173154,"journal":{"name":"Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123134301","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: 76

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval 2019 ACM SIGIR信息检索理论国际会议论文集

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 1900-01-01 DOI: 10.1145/3341981

引用次数: 2