The World Wide Web Conference最新文献_第10页

Transfer Learning for Unsupervised Influenza-like Illness Models from Online Search Data 基于在线搜索数据的无监督类流感疾病模型的迁移学习

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313477

Bin Zou, Vasileios Lampos, I. Cox

{"title":"Transfer Learning for Unsupervised Influenza-like Illness Models from Online Search Data","authors":"Bin Zou, Vasileios Lampos, I. Cox","doi":"10.1145/3308558.3313477","DOIUrl":"https://doi.org/10.1145/3308558.3313477","url":null,"abstract":"A considerable body of research has demonstrated that online search data can be used to complement current syndromic surveillance systems. The vast majority of previous work proposes solutions that are based on supervised learning paradigms, in which historical disease rates are required for training a model. However, for many geographical regions this information is either sparse or not available due to a poor health infrastructure. It is these regions that have the most to benefit from inferring population health statistics from online user search activity. To address this issue, we propose a statistical framework in which we first learn a supervised model for a region with adequate historical disease rates, and then transfer it to a target region, where no syndromic surveillance data exists. This transfer learning solution consists of three steps: (i) learn a regularized regression model for a source country, (ii) map the source queries to target ones using semantic and temporal similarity metrics, and (iii) re-adjust the weights of the target queries. It is evaluated on the task of estimating influenza-like illness (ILI) rates. We learn a source model for the United States, and subsequently transfer it to three other countries, namely France, Spain and Australia. Overall, the transferred (unsupervised) models achieve strong performance in terms of Pearson correlation with the ground truth (> .92 on average), and their mean absolute error does not deviate greatly from a fully supervised baseline.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81398195","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: 14

Before and After GDPR: The Changes in Third Party Presence at Public and Private European Websites GDPR前后:欧洲公共和私人网站中第三方存在的变化

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313524

J. Sørensen, Sokol Kosta

引用次数: 68

Predictive Crawling for Commercial Web Content 预测抓取商业Web内容

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313694

Shuguang Han, Bernhard Brodowsky, Przemek Gajda, Sergey Novikov, Michael Bendersky, Marc Najork, R. Dua, Alexandrin Popescul

引用次数: 3

How Intention Informed Recommendations Modulate Choices: A Field Study of Spoken Word Content 意向建议如何调节选择:口语内容的实地研究

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313540

Longqi Yang, Michael Sobolev, Yu Wang, Jen-Tseng Chen, D. Dunne, Christina Tsangouri, Nicola Dell, Mor Naaman, D. Estrin

{"title":"How Intention Informed Recommendations Modulate Choices: A Field Study of Spoken Word Content","authors":"Longqi Yang, Michael Sobolev, Yu Wang, Jen-Tseng Chen, D. Dunne, Christina Tsangouri, Nicola Dell, Mor Naaman, D. Estrin","doi":"10.1145/3308558.3313540","DOIUrl":"https://doi.org/10.1145/3308558.3313540","url":null,"abstract":"People's content choices are ideally driven by their intentions, aspirations, and plans. However, in reality, choices may be modulated by recommendation systems which are typically trained to promote popular items and to reinforce users' historical behavior. As a result, the utility and user experience of content consumption can be affected implicitly and undesirably. To study this problem, we conducted a 2 × 2 randomized controlled field experiment (105 urban college students) to compare the effects of intention informed recommendations with classical intention agnostic systems. The study was conducted in the context of spoken word web content (podcasts) which is often consumed through subscription sites or apps. We modified a commercial podcast app to include (1) a recommender that takes into account users' stated intentions at onboarding, and (2) a Collaborative Filtering (CF) recommender during daily use. Our study suggests that: (1) intention-aware recommendations can significantly raise users' interactions (subscriptions and listening) with channels and episodes related to intended topics by over 24%, even if such a recommender is only used during onboarding, and (2) the CF-based recommender doubles users' explorations on episodes from not-subscribed channels and improves satisfaction for users onboarded with the intention-aware recommender.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82953435","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: 13

Modeling Heart Rate and Activity Data for Personalized Fitness Recommendation 为个性化健身建议建模心率和活动数据

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313643

Jianmo Ni, Larry Muhlstein, Julian McAuley

{"title":"Modeling Heart Rate and Activity Data for Personalized Fitness Recommendation","authors":"Jianmo Ni, Larry Muhlstein, Julian McAuley","doi":"10.1145/3308558.3313643","DOIUrl":"https://doi.org/10.1145/3308558.3313643","url":null,"abstract":"Activity logs collected from wearable devices (e.g. Apple Watch, Fitbit, etc.) are a promising source of data to facilitate a wide range of applications such as personalized exercise scheduling, workout recommendation, and heart rate anomaly detection. However, such data are heterogeneous, noisy, diverse in scale and resolution, and have complex interdependencies, making them challenging to model. In this paper, we develop context-aware sequential models to capture the personalized and temporal patterns of fitness data. Specifically, we propose FitRec - an LSTM-based model that captures two levels of context information: context within a specific activity, and context across a user's activity history. We are specifically interested in (a) estimating a user's heart rate profile for a candidate activity; and (b) predicting and recommending suitable activities on this basis. We evaluate our model on a novel dataset containing over 250 thousand workout records coupled with hundreds of millions of parallel sensor measurements (e.g. heart rate, GPS) and metadata. We demonstrate that the model is able to learn contextual, personalized, and activity-specific dynamics of users' heart rate profiles during exercise. We evaluate the proposed model against baselines on several personalized recommendation tasks, showing the promise of using wearable data for activity modeling and recommendation.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83400712","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: 68

Automatic Boolean Query Refinement for Systematic Review Literature Search 系统综述文献检索的自动布尔查询细化

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313544

Harrisen Scells, G. Zuccon, B. Koopman

{"title":"Automatic Boolean Query Refinement for Systematic Review Literature Search","authors":"Harrisen Scells, G. Zuccon, B. Koopman","doi":"10.1145/3308558.3313544","DOIUrl":"https://doi.org/10.1145/3308558.3313544","url":null,"abstract":"In the medical domain, systematic reviews are a highly trustworthy evidence source used to inform clinical diagnosis and treatment, and governmental policy making. Systematic reviews must be complete in that all relevant literature for the research question of the review must be synthesised in order to produce a recommendation. To identify the literature to screen for inclusion in systematic reviews, information specialists construct complex Boolean queries that capture the information needs defined by the research questions of the systemic review. However, in the quest for total recall, these Boolean queries return many non relevant results. In this paper, we propose automatic methods for Boolean query refinement in the context of systematic review literature retrieval with the aim of alleviating this high-recall, low-precision problem. To do this, we build upon current literature and define additional semantic transformations for Boolean queries in the form of query expansion and reduction. Empirical evaluation is done on a set of real systematic review queries to show how our method performs in a realistic setting. We found that query refinement strategies produced queries that were more effective than the original in terms of six information retrieval evaluation measures. In particular, queries were refined to increase precision, while maintaining, or even increasing, recall - this, in turn, translates into both time and cost savings when creating laborious and expensive systematic reviews.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91376852","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: 32

On-Device Algorithms for Public-Private Data with Absolute Privacy 具有绝对隐私的公私数据的设备上算法

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313677

Alessandro Epasto, Hossein Esfandiari, V. Mirrokni

{"title":"On-Device Algorithms for Public-Private Data with Absolute Privacy","authors":"Alessandro Epasto, Hossein Esfandiari, V. Mirrokni","doi":"10.1145/3308558.3313677","DOIUrl":"https://doi.org/10.1145/3308558.3313677","url":null,"abstract":"Motivated by the increasing need to preserve privacy in digital devices, we introduce the on-device public-private model of computation. Our motivation comes from social-network based recommender systems in which the users want to receive recommendations based on the information available on their devices, as well as the suggestions of their social contacts, without sharing such information or contacts with the central recommendation system. Our model allows us to solve many algorithmic problems while providing absolute (deterministic) guarantees of the privacy of on-device data and the user's contacts. In fact, we ensure that the private data and private contacts are never revealed to the central system. Our restrictive model of computation presents several interesting algorithmic challenges because any computation based on private information and contacts must be performed on local devices of limited capabilities. Despite these challenges, under realistic assumptions of inter-device communication, we show several efficient algorithms for fundamental data mining and machine learning problems, ranging from k-means clustering to heavy hitters. We complement this analysis with strong impossibility results for efficient private algorithms without allowing inter-device communication. In our experimental evaluation, we show that our private algorithms provide results almost as accurate as those of the non-private ones while speeding up the on-device computations by orders of magnitude.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88207038","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: 3

Judging a Book by Its Cover: The Effect of Facial Perception on Centrality in Social Networks 以貌取人:社交网络中面部知觉对中心性的影响

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313527

Dongyu Zhang, Teng Guo, Hanxiao Pan, Jie Hou, Zhitao Feng, Liang Yang, Hongfei Lin, Feng Xia

{"title":"Judging a Book by Its Cover: The Effect of Facial Perception on Centrality in Social Networks","authors":"Dongyu Zhang, Teng Guo, Hanxiao Pan, Jie Hou, Zhitao Feng, Liang Yang, Hongfei Lin, Feng Xia","doi":"10.1145/3308558.3313527","DOIUrl":"https://doi.org/10.1145/3308558.3313527","url":null,"abstract":"Facial appearance matters in social networks. Individuals frequently make trait judgments from facial clues. Although these face-based impressions lack the evidence to determine validity, they are of vital importance, because they may relate to human network-based social behavior, such as seeking certain individuals for help, advice, dating, and cooperation, and thus they may relate to centrality in social networks. However, little to no work has investigated the apparent facial traits that influence network centrality, despite the large amount of research on attributions of the central position including personality and behavior. In this paper, we examine whether perceived traits based on facial appearance affect network centrality by exploring the initial stage of social network formation in a first-year college residential area. We took face photos of participants who are freshmen living in the same residential area, and we asked them to nominate community members linking to different networks. We then collected facial perception data by requiring other participants to rate facial images for three main attributions: dominance, trustworthiness, and attractiveness. Meanwhile, we proposed a framework to discover how facial appearance affects social networks. Our results revealed that perceived facial traits were correlated with the network centrality and that they were indicative to predict the centrality of people in different networks. Our findings provide psychological evidence regarding the interaction between faces and network centrality. Our findings also offer insights in to a combination of psychological and social network techniques, and they highlight the function of facial bias in cuing and signaling social traits. To the best of our knowledge, we are the first to explore the influence of facial perception on centrality in social networks.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73207706","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: 10

Reply-Aided Detection of Misinformation via Bayesian Deep Learning 基于贝叶斯深度学习的错误信息回复辅助检测

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313718

Qiang Zhang, Aldo Lipani, Shangsong Liang, Emine Yilmaz

{"title":"Reply-Aided Detection of Misinformation via Bayesian Deep Learning","authors":"Qiang Zhang, Aldo Lipani, Shangsong Liang, Emine Yilmaz","doi":"10.1145/3308558.3313718","DOIUrl":"https://doi.org/10.1145/3308558.3313718","url":null,"abstract":"Social media platforms are a plethora of misinformation and its potential negative influence on the public is a growing concern. This concern has drawn the attention of the research community on developing mechanisms to detect misinformation. The task of misinformation detection consists of classifying whether a claim is True or False. Most research concentrates on developing machine learning models, such as neural networks, that outputs a single value in order to predict the veracity of a claim. One of the major problem faced by these models is the inability of representing the uncertainty of the prediction, which is due incomplete or finite available information about the claim being examined. We address this problem by proposing a Bayesian deep learning model. The Bayesian model outputs a distribution used to represent both the prediction and its uncertainty. In addition to the claim content, we also encode auxiliary information given by people's replies to the claim. First, the model encodes a claim to be verified, and generate a prior belief distribution from which we sample a latent variable. Second, the model encodes all the people's replies to the claim in a temporal order through a Long Short Term Memory network in order to summarize their content. This summary is then used to update the prior belief generating the posterior belief. Moreover, in order to train this model, we develop a Stochastic Gradient Variational Bayes algorithm to approximate the analytically intractable posterior distribution. Experiments conducted on two public datasets demonstrate that our model outperforms the state-of-the-art detection models.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77505738","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: 54

Multilevel Network Alignment 多层网络对准

The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313484

Si Zhang, Hanghang Tong, Ross Maciejewski, Tina Eliassi-Rad

{"title":"Multilevel Network Alignment","authors":"Si Zhang, Hanghang Tong, Ross Maciejewski, Tina Eliassi-Rad","doi":"10.1145/3308558.3313484","DOIUrl":"https://doi.org/10.1145/3308558.3313484","url":null,"abstract":"Network alignment, which aims to find the node correspondence across multiple networks, is a fundamental task in many areas, ranging from social network analysis to adversarial activity detection. The state-of-the-art in the data mining community often view the node correspondence as a probabilistic cross-network node similarity, and thus inevitably introduce an O(n2) lower bound on the computational complexity. Moreover, they might ignore the rich patterns (e.g., clusters) accompanying the real networks. In this paper, we propose a multilevel network alignment algorithm (Moana) which consists of three key steps. It first efficiently coarsens the input networks into their structured representations, and then aligns the coarsest representations of the input networks, followed by the interpolations to obtain the alignment at multiple levels including the node level at the finest granularity. The proposed coarsen-align-interpolate method bears two key advantages. First, it overcomes the O(n2) lower bound, achieving a linear complexity. Second, it helps reveal the alignment between rich patterns of the input networks at multiple levels (e.g., node, clusters, super-clusters, etc.). Extensive experimental evaluations demonstrate the efficacy of the proposed algorithm on both the node-level alignment and the alignment among rich patterns (e.g., clusters) at different granularities.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74256495","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: 45