Companion Proceedings of the The Web Conference 2018最新文献_第3页

The Shifting Landscape of Web Search and Mining: Past, Present, and Future 网络搜索和挖掘的变化:过去、现在和未来

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3188749

Davood Rafiei, Eugene Agichtein, R. Baeza-Yates, J. Kleinberg, J. Leskovec

{"title":"The Shifting Landscape of Web Search and Mining: Past, Present, and Future","authors":"Davood Rafiei, Eugene Agichtein, R. Baeza-Yates, J. Kleinberg, J. Leskovec","doi":"10.1145/3184558.3188749","DOIUrl":"https://doi.org/10.1145/3184558.3188749","url":null,"abstract":"The Web's content has been going through major changes, triggered by multiple factors including changes in user demographic and authoring behaviour, a shift in device types that access the Web, and changes in common use cases of the Web. More specifically, the number of mobile internet users has surpassed the desktop users according to different statistics; a considerable portion of web use cases are in the form of social interactions rather than information seeking; and the authoring behaviour has transformed from compiling a page and linking resources to sharing content with like-minded followers and leaving likes and comments on posts. Those changes have influenced and are expected to shape the way the content is organized, searched, ranked and analyzed. This panel brings together researchers who have been working in different established areas related to web search and mining, web content and social network analysis, and semantics and knowledge management. The panel will draw from the experience of the panellists, dealing with changes in their respective fields. In the first (role-playing) round, each panellist will strongly take a side on where the changes are heading, arguing that one form of content will dominate in the near future. In the second round, the panellists will counter each other and will share their vision on what future holds in terms of research problems and directions. The members of the audience will participate, in a QA session with the panellists, bringing their own perspectives to the discussion.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127922951","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: 0

From Alt-Right to Alt-Rechts: Twitter Analysis of the 2017 German Federal Election 从另类右翼到另类右翼:2017年德国联邦选举的推特分析

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3188733

Fred Morstatter, Yunqiu Shao, A. Galstyan, S. Karunasekera

引用次数: 42

Handling Confounding for Realistic Off-Policy Evaluation 为现实的非政策评估处理混淆

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186915

Saurabh Sohoney, Nikita Prabhu, V. Chaoji

引用次数: 0

Learning Large Scale Ordinal Ranking Model via Divide-and-Conquer Technique 用分治法学习大规模有序排序模型

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191658

Lu Tang, Sougata Chaudhuri, A. Bagherjeiran, Lingzhi Zhou

{"title":"Learning Large Scale Ordinal Ranking Model via Divide-and-Conquer Technique","authors":"Lu Tang, Sougata Chaudhuri, A. Bagherjeiran, Lingzhi Zhou","doi":"10.1145/3184558.3191658","DOIUrl":"https://doi.org/10.1145/3184558.3191658","url":null,"abstract":"Structured prediction, where outcomes have a precedence order, lies at the heart of machine learning for information retrieval, movie recommendation, product review prediction, and digital advertising. Ordinal ranking, in particular, assumes that the structured response has a linear ranked order. Due to the extensive applicability of these models, substantial research has been devoted to understanding them, as well as developing efficient training techniques. One popular and widely cited technique of training ordinal ranking models is to exploit the linear precedence order and systematically reduce it to a binary classification problem. This facilitates the usage of readily available, powerful binary classifiers, but necessitates an expansion of the original training data, where the training data increases by $K-1$ times of its original size, with K being the number of ordinal classes. Due to prevalent nature of problems with large number of ordered classes, the reduction leads to datasets which are too large to train on single machines. While approximation methods like stochastic gradient descent are typically applied here, we investigate exact optimization solutions that can scale. In this paper, we present a divide-and-conquer (DC) algorithm, which divides large scale binary classification data into a cluster of machines and trains logistic models in parallel, and combines them at the end of the training phase to create a single binary classifier, which can then be used as an ordinal ranker. It requires no synchronization between the parallel learning algorithms during the training period, which makes training on large datasets feasible and efficient. We prove consistency and asymptotic normality property of the learned models using our proposed algorithm. We provide empirical evidence, on various ordinal datasets, of improved estimation and prediction performance of the model learnt using our algorithm, over several standard divide-and-conquer algorithms.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132382495","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

Selection Bias in News Coverage: Learning it, Fighting it 新闻报道中的选择偏见:了解它，对抗它

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3188724

Dylan Bourgeois, Jérémie Rappaz, K. Aberer

{"title":"Selection Bias in News Coverage: Learning it, Fighting it","authors":"Dylan Bourgeois, Jérémie Rappaz, K. Aberer","doi":"10.1145/3184558.3188724","DOIUrl":"https://doi.org/10.1145/3184558.3188724","url":null,"abstract":"News entities must select and filter the coverage they broadcast through their respective channels since the set of world events is too large to be treated exhaustively. The subjective nature of this filtering induces biases due to, among other things, resource constraints, editorial guidelines, ideological affinities, or even the fragmented nature of the information at a journalist's disposal. The magnitude and direction of these biases are, however, widely unknown. The absence of ground truth, the sheer size of the event space, or the lack of an exhaustive set of absolute features to measure make it difficult to observe the bias directly, to characterize the leaning's nature and to factor it out to ensure a neutral coverage of the news. In this work, we introduce a methodology to capture the latent structure of media's decision process on a large scale. Our contribution is multi-fold. First, we show media coverage to be predictable using personalization techniques, and evaluate our approach on a large set of events collected from the GDELT database. We then show that a personalized and parametrized approach not only exhibits higher accuracy in coverage prediction, but also provides an interpretable representation of the selection bias. Last, we propose a method able to select a set of sources by leveraging the latent representation. These selected sources provide a more diverse and egalitarian coverage, all while retaining the most actively covered events.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"449 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132500103","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: 17

Travel Itinerary Recommendations with Must-see Points-of-Interest 旅游路线推荐，必看景点

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191558

Kendall Taylor, Kwan Hui Lim, Jeffrey Chan

{"title":"Travel Itinerary Recommendations with Must-see Points-of-Interest","authors":"Kendall Taylor, Kwan Hui Lim, Jeffrey Chan","doi":"10.1145/3184558.3191558","DOIUrl":"https://doi.org/10.1145/3184558.3191558","url":null,"abstract":"Travelling and touring are popular leisure activities enjoyed by millions of tourists around the world. However, the task of travel itinerary recommendation and planning is tedious and challenging for tourists, who are often unfamiliar with the various Points-of-Interest (POIs) in a city. Apart from identifying popular POIs, the tourist needs to construct a travel itinerary comprising a subset of these POIs, and to order these POIs as a sequence of visits that can be completed within his/her available touring time. For a more realistic itinerary, the tourist also has to account for travelling time between POIs and visiting times at individual POIs. Furthermore, this itinerary should incorporate tourist preferences such as desired starting and ending POIs (e.g., POIs that are near the tourist's hotel) and a subset of must-see POIs (e.g., popular POIs that a tourist must visit). We term this the TourMustSee problem, which is based on a variant of the Orienteering problem. Following which, we propose the LP+M algorithm for solving the TourMustSee problem as an Integer Linear Program (ILP). Using a Flickr dataset of POI visits in seven touristic cities, we compare LP+M against various ILP-based baselines, and the results show that LP+M recommends better travel itineraries in terms of POI popularity, total POIs visited, total touring time utilized and must-visit POI(s) inclusion.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133932747","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: 49

Smart Cities at Risk!: Privacy and Security Borderlines from Social Networking in Cities 智慧城市面临风险!:城市社交网络的隐私和安全边界

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191516

Vaia Moustaka, Zenonas Theodosiou, A. Vakali, A. Kounoudes

引用次数: 16

Machine Learning for the Peer Assessment Credibility 基于机器学习的同行评估可信度

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186957

Yingru Lin, S. Han, B. Kang

引用次数: 1

An Efficient Immunization Strategy Using Overlapping Nodes and Its Neighborhoods 利用重叠节点及其邻域的有效免疫策略

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191566

Manish Kumar, Anurag Singh, H. Cherifi

{"title":"An Efficient Immunization Strategy Using Overlapping Nodes and Its Neighborhoods","authors":"Manish Kumar, Anurag Singh, H. Cherifi","doi":"10.1145/3184558.3191566","DOIUrl":"https://doi.org/10.1145/3184558.3191566","url":null,"abstract":"When an epidemic occurs, it is often impossible to vaccinate the entire population due to limited amount of resources. Therefore, it is of prime interest to identify the set of influential spreaders to immunize, in order to minimize both the cost of vaccine resource and the disease spreading. While various strategies based on the network topology have been introduced, few works consider the influence of the community structure in the epidemic spreading process. Nowadays, it is clear that many real-world networks exhibit an overlapping community structure, in which nodes are allowed to belong to more than one community. Previous work shows that the numbers of communities to which a node belongs is a good measure of its epidemic influence. In this work, we address the effect of nodes in the neighborhood of the overlapping nodes on epidemics spreading. The proposed immunization strategy provides highly connected neighbors of overlapping nodes in the network to immunize. The whole process requires information only at the node level and is well suited to large-scale networks. Extensive experiments on four real-world networks of diverse nature have been performed. Comparisons with alternative local immunization strategies using the fraction of the Largest Connected Component (LCC) after immunization,show that the proposed method is much more efficient. Additionally, it compares favorably to global measures such as degree and betweenness centrality.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132622287","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

The WWW (and an H) of Mobile Application Usage in the City: The What, Where, When, and How 城市中移动应用使用的WWW(和H):什么，在哪里，何时，如何

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191561

Eduardo Graells-Garrido, Diego Caro, Omar Miranda, R. Schifanella, Oscar F. Peredo

{"title":"The WWW (and an H) of Mobile Application Usage in the City: The What, Where, When, and How","authors":"Eduardo Graells-Garrido, Diego Caro, Omar Miranda, R. Schifanella, Oscar F. Peredo","doi":"10.1145/3184558.3191561","DOIUrl":"https://doi.org/10.1145/3184558.3191561","url":null,"abstract":"People fulfill their informational needs through smartphones, however, little is known regarding how the urban fabric and the activities that take place in it affect the usage of mobile applications. In this regard, starting from an anonymized dataset of Deep Packet Inspection (DPI) data from the largest telecommunications operator in Chile, we focus on the following questions: What are the most popular applications used in the city Where are they spatially clustered When does an application is more frequently used And How does the urban context and the mobility patterns relate to application usage As a result, we observed that specific applications present high spatial clustering, while the most popular services are geographically dispersed throughout the entire city. Clusters appear in places of high floating population; however, hotspots vary in space depending on the application. Interestingly, we found that commuting plays an important role, both in terms of rush hours and transportation infrastructure. We present a discussion on these results, focusing on how the physical space and the daily commuting routine affect the pattern of data consumption and represent an important aspect in mobile users behavioral studies.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133664225","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: 17