OpenRaiser/Intern-Atlas huggingface dataset:
Attention Convolutional Neural Network for Advertiser-level Click-through Rate Forecasting
Authors: Hongchang Gao, Deguang Kong, Miao Lu, Xiao Bai, Jian Yang
Venue: WWW
Year: 2018

Click-through rate (CTR) is a critical problem in online advertising. Most existing researches only focus on the user-level CTR prediction. However, advertiser-level CTR forecasting also plays a very important role because advertisers typically decide how much they would like to bid for advertisements to achieve the maximum clicks given their budget based on CTR forecasting. Over-forecasting will make the advertiser to pay more than necessary but get less return on investment (ROI). Under-forecasting will make the advertiser to spend less money on campaigns but they cannot achieve the desired ROI goals. In this paper, we focus on the advertiser-level CTR forecasting and formulate it as a time series forecasting problem based on the historical CTR record. This is a very challenging problem due to the heavy fluctuation and highly non-linearity of time series. Furthermore, advertisers usually provide useful contextual information for their campaigns, such as text descriptions, targeting locations and devices, which has high correlation with CTR but has not yet been used for CTR forecasting. Thus, we propose a novel context-aware attention convolutional neural network (CACNN), which can capture the high non-linearity and local information of the time series, as well as the underlying correlation between the time series of CTR and the contextual information. To the best of our knowledge, this is the first work employing convolutional neural network and incorporating heterogeneous information to perform CTR forecasting at advertiser level. We implement the system on Yahoo TensorFlowOnSpark platform which enables distributed deep learning on a cluster of GPU and CPU servers, and achieves faster learning speed and data access on HDFS when available. The effectiveness of CACNN model has been demonstrated in real-world Yahoo advertising dataset, and therefore deployed in production with daily rolling of the model.
Semantic Annotation in the Biomedical Domain: Large-scale Classification and BioASQ
Authors: Éric Gaussier
Venue: WWW
Year: 2018

Semantic annotation in the biomedical domain raises the problem of classifying texts with large-scale taxonomies, a problem sometimes referred to as extreme classification. In this presentation, we will give an overview of this problem and the main solutions proposed, with a focus on textual collections and the BioASQ challenge.
Concept Embedded Topic Modeling Technique
Authors: Dakshi Tharanga Kapugama Geeganage
Venue: WWW
Year: 2018

Text contents are overloaded with the digitization of the data and new contents are transmitted through many sources by generating a large volume of information, which spreads all over the world through different communication media. Therefore, text data is available everywhere and reading, understanding and analysing the text data has become a main activity in daily routine. With the increment of the volume and the variety of information, organizing and searching, the required information has become vital. Topic modelling is the state of the art for information organization, understanding and extracting the content. Most of the prevailing topic models use the probabilistic approaches and consider the frequency and the co-occurrence to discover the topics from collections of documents. The proposed research aims to address the existing problems of topic modeling by introducing a concept embedded topic model which generates the most relevant and meaningful topics by understanding the content. The research includes approaches to understand the semantic elements from the content, domain identification of concepts and provide most suitable topics without getting the number of topics from the user beforehand. Capturing the semantics of document collections and generating the most related set of topics according to the actual meaning will be the significance of this research.
Class Specific TF-IDF Boosting for Short-text Classification: Application to Short-texts Generated During Disasters
Authors: Samujjwal Ghosh, Maunendra Sankar Desarkar
Venue: WWW
Year: 2018

Proper formulation of features plays an important role in short-text classification tasks as the amount of text available is very little. In literature, Term Frequency - Inverse Document Frequency (TF-IDF) is commonly used to create feature vectors for such tasks. However, TF-IDF formulation does not utilize the class information available in supervised learning. For classification problems, if it is possible to identify terms that can strongly distinguish among classes, then more weight can be given to those terms during feature construction phase. This may result in improved classifier performance with the incorporation of extra class label related information. We propose a supervised feature construction method to classify tweets, based on the actionable information that might be present, posted during different disaster scenarios. Improved classifier performance for such classification tasks can be helpful in the rescue and relief operations. We used three benchmark datasets containing tweets posted during Nepal and Italy earthquakes in 2015 and 2016 respectively. Experimental results show that the proposed method obtains better classification performance on these benchmark datasets.
Activity-based Mobility Profiling: A Purely Temporal Modeling Approach
Authors: Shreya Ghosh, Soumya K. Ghosh, Rahul Deb Das, Stephan Winter
Venue: WWW
Year: 2018

Several studies have shown that the spatio-temporal mobility traces of human movements can be used to identify an individual. However, this work presents a novel framework for activity-based mobility profiling of individuals using only the temporal information. The proposed framework is conducive to model individuals' activity patterns in temporal scale, and quantifies the uniqueness measures based on certain temporal features of the activity sequence.
SoBigData: Social Mining & Big Data Ecosystem
Authors: Fosca Giannotti, Roberto Trasarti, Kalina Bontcheva, Valerio Grossi
Venue: WWW
Year: 2018

One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: (i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; (ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; (iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.
Analysis of Subtopic Discovery Algorithms for Real-time Information Summarization
Authors: Gustavo Gonçalves, Flávio Martins, João Magalhães
Venue: WWW
Year: 2018

The rise of large data streams introduces new challenges regarding the delivery of relevant content towards an information need. This need can be seen as a broad topic of information. By identifying sub-streams within a broader data stream, we can retrieve relevant content that matches the multiple facets of the topic; thus summarizing information, and matching the initial need. In this paper, we propose to study the generation of sub-streams over time and compare various aggregation methods to summarize information. Our experiments were made using the standard TREC Real-Time Summarization (RTS) 2017 dataset.
A Corpus for Hybrid Question Answering Systems
Authors: Brigitte Grau, Anne-Laure Ligozat
Venue: WWW
Year: 2018

Question answering has been the focus of a lot of researches and evaluation campaigns, either for text-based systems (TREC and CLEF evaluation campaigns for example), or for knowledge-based systems (QALD, BioASQ). Few systems have effectively combined both types of resources and methods in order to exploit the fruitfulness of merging the two kinds of information repositories. The only evaluation QA track that focuses on hybrid QA is QALD since 2014. As it is a recent task, few annotated data are available (around 150 questions). In this paper, we present a question answering dataset that was constructed to develop and evaluate hybrid question answering systems. In order to create this corpus, we collected several textual corpora and augmented them with entities and relations of a knowledge base by retrieving paths in the knowledge base which allow to answer the questions. The resulting corpus contains 4300 question-answer pairs and 1600 have a true link with DBpedia.
Super-blockers and the Effect of Network Structure on Information Cascades
Authors: Caitlin Gray, Lewis Mitchell, Matthew Roughan
Venue: WWW
Year: 2018

Modelling information cascades over online social networks is important in fields from marketing to civil unrest prediction, however the underlying network structure strongly affects the probability and nature of such cascades. Even with simple cascade dynamics the probability of large cascades are almost entirely dictated by network properties, with well-known networks such as Erdos-Renyi and Barabasi-Albert producing wildly different cascades from the same model. Indeed, the notion of 'superspreaders' has arisen to describe highly influential nodes promoting global cascades in a social network. Here we use a simple model of global cascades to show that the presence of locality in the network increases the probability of a global cascade due to the increased vulnerability of connecting nodes. Rather than 'super-spreaders', we find that the presence of these highly connected 'super-blockers' in heavy-tailed networks in fact reduces the probability of global cascades, while promoting information spread when targeted as the initial spreader.
Simple vs Optimal Contests with Convex Costs
Authors: Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis
Venue: WWW
Year: 2018

We study an optimal contest design problem where contributors abilities are private, their costs are convex as a function of their effort, and the designer seeks to maximize their total productivity. We address the design of approximately-optimal mechanisms that are robust, in that they are independent of the ability distribution and the precise form of the cost function. We show that a very simple all-pay contest where the prize is distributed equally among the top quartile of contributors is a constant-factor approximation to the optimal, for a large class of convex cost functions, when the number of contributors is larger than some constant. This result stands in contrast to contests with linear costs, where awarding a prize to a single top contributor ("winner-takes-all»») is approximately-optimal; when costs are convex, winner-takes-all is far from optimal. We validate the performance of our approximately-optimal contest designs via simulation experiments, which uncover much better empirical performance than the worst-case guarantees. Our results are enabled by novel results in the space of optimal mechanism design with convex costs, which could be of independent interest.
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
Authors: Nina Grgic-Hlaca, Elissa M. Redmiles, Krishna P. Gummadi, Adrian Weller
Venue: WWW
Year: 2018

As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people»s moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person»s assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people»s unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people»s fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.
Identifying Modes of User Engagement with Online News and Their Relationship to Information Gain in Text
Authors: Nir Grinberg
Venue: WWW
Year: 2018

Prior work established the benefits of server-recorded user engagement measures (e.g. clickthrough rates) for improving the results of search engines and recommendation systems. Client-side measures of post-click behavior received relatively little attention despite the fact that publishers have now the ability to measure how millions of people interact with their content at a fine resolution using client-side logging. In this study, we examine patterns of user engagement in a large, client-side log dataset of over 7.7 million page views (including both mobile and non-mobile devices) of 66,821 news articles from seven popular news publishers. For each page view we use three summary statistics: dwell time, the furthest position the user reached on the page, and the amount of interaction with the page through any form of input (touch, mouse move, etc.). We show that simple transformations on these summary statistics reveal six prototypical modes of reading that range from scanning to extensive reading and persist across sites. Furthermore, we develop a novel measure of information gain in text to capture the development of ideas within the body of articles and investigate how information gain relates to the engagement with articles. Finally, we show that our new measure of information gain is particularly useful for predicting reading of news articles before publication, and that the measure captures unique information not available otherwise.
Demo Track Chairs' Welcome & Organization
Authors: Paul Groth, Amelie Gyrard
Venue: WWW
Year: 2018

The Demo Track is one of the most exciting parts of any Web Conference. It allows researchers and practitioners to demonstrate new systems in an engaging and hands-on manner to the community. The Web has been driven forward by building systems and technology. The demo track is a venue that encourages this sort of important type of result This year the track received 71 submissions of those 30 were accepted for a 42% accept rate. We had a comprehensive review procedure that looked at a number of dimensions including the novelty of the demo, its fit with the conference, its research content, and its potential for audience engagement. We were pleased by the number of submissions that included links to online demonstrations and/or videos. This gave reviewers additional information about how the demo would be presented. Overall, we had 232 reviews across all submissions. Many of the reviews provided not only a their expert judgement but ways in which the submissions could be improved. It is often difficult judging demonstrations as there are multiple factors to be taken into account. We want to thank the entire committee for taking the time to support the track. The resulting set of selected demos reflects the wide-variety of technology and research interests impacting the wide. Demonstrations cover topics such as using data on the web, the integration of the web and the physical world, knowledge graphs, search engines, security and privacy, and dealing with multimedia data. We believe that these demos provide an exciting taste of the future of the Web.
Monetization Strategies for the Web of Data
Authors: Tobias Grubenmann
Venue: WWW
Year: 2018

Inspired by the World Wide Web, the Web of Data is a network of interlinked data fragments. One of the main advantages of the Web of Data is that all of its content is processable by machines. However, this also has its drawbacks when it comes to monetization of the content: advertisements and donationstwo important financial motors in the World Wide Webdo not translate into the Web of Data as they rely on exposing the user to advertisement/call for donations. The remedy this situation, we propose two different monetization strategies for the Web of Data. The first strategy involves a marketplace where users can buy data in an integrated way. The second strategy allows third parties to promote certain data. In return, the sponsors pay money whenever a user follows a link contained in the sponsored data. We identified two different kind of datacommercial and sponsored datawhich can benefit from the two respective monetization strategies. With our work, we propose solutions to the problem of financing the creation and maintenance of content in the Web of Data.
Financing the Web of Data with Delayed-Answer Auctions
Authors: Tobias Grubenmann, Abraham Bernstein, Dmitry Moor, Sven Seuken
Venue: WWW
Year: 2018

The World Wide Web is a massive network of interlinked documents. One of the reasons the World Wide Web is so successful is the fact that most content is available free of any charge. Inspired by the success of the World Wide Web, the Web of Data applies the same strategy of interlinking to data. To this point, most of data in the Web of Data is also free of charge. The fact that the data is freely available raises the question of financing these services, however. As we will discuss in this paper, advertisement and donations cannot easily be applied to this new setting. To create incentives to subsidize data providers, we propose that sponsors should pay the providers to promote sponsored data. In return, sponsored data will be privileged over non-sponsored data. Since it is not possible to enforce a certain ordering on the data the user will receive, we propose to split up the data into different batches and deliver these batches with different delays. In this way, we can privilege sponsored data without withholding any non-sponsored data from the user. In this paper, we introduce a new concept of a delayed-answer auction, where sponsors can pay to prioritize their data. We introduce a new model which captures the particular situation when a user access data in the Web of Data. We show how the weighted Vickrey-Clarke-Groves auction mechanism can be applied to our scenario and we discuss how certain parameters can influence the nature of our auction. With our new concept, we build a first step to a free yet financial sustainable Web of Data.
RaRE: Social Rank Regulated Large-scale Network Embedding
Authors: Yupeng Gu, Yizhou Sun, Yanen Li, Yang Yang
Venue: WWW
Year: 2018

Network embedding algorithms that map nodes in a network into a low-dimensional vector space are prevalent in recent years, due to their superior performance in many network-based tasks, such as clustering, classification, and link prediction. The main assumption of existing algorithms is that the learned latent representation for nodes should preserve the structure of the network, in terms of first-order or higher-order connectivity. In other words, nodes that are more similar will have higher probability to connect to each other. This phenomena is typically explained as homophily in network science. However, there is another factor usually neglected by the existing embedding algorithms, which is the popularity of a node. For example, celebrities in a social network usually receive numerous followers, which cannot be fully explained by the similarity of the two users. We denote this factor with the terminology "social rank»». We then propose a network embedding model that considers both of the two factors in link generation, and learn proximity-based embedding and social rank-based embedding separately. Rather than simply treating these two factors independent with each other, a carefully designed link generation model is proposed, which explicitly models the interdependency between these two types of embeddings. Experiments on several real-world datasets across different domains demonstrate the superiority of our novel network embedding model over the state-of-the-art methods.
Readers' Demanded Hyperlink Prediction in Wikipedia
Authors: Laxmi Amulya Gundala, Francesca Spezzano
Venue: WWW
Year: 2018

In this paper, we describe our on-going research on the problem of predicting needed hyperlinks between pairs of Wikipedia pages (u,v) that are not connected, yet show readers' search navigation from u to v. We propose a solution that first estimates how long will these searches last and then predicts new hyperlinks according to descending order of duration. Our initial experimental results show that our best solution achieves an AUROC of 0.77 on the Wikipedia Clickstream dataset and a [email protected]% of 1.0 and significantly beats the baselines.
Which Algorithm Performs Best: Algorithm Selection for Community Detection
Authors: Gaoyang Guo, Chaokun Wang, Xiang Ying
Venue: WWW
Year: 2018

A myriad of community detection methods have been designed to discover communities based on specific network features in different disciplines, such as sociology, physics, and computer science. Consequentially, we have to face the problem of Algorithm Selection for Community Detection (ASCD): Given a specific network, which algorithm should we select to reveal its latent community structures In this study, we propose a model called CYDES to address the ASCD problem. CYDES consists of two parts, namely feature matrix generation and algorithm classification. We combine three effective feature extraction methods with the idea of BOW model to construct a fixed-size feature matrix. After a nonlinear transformation to the feature matrix, a softmax regression model is utilized to generate a classification label representing the best community detection algorithm we select. Extensive experimental results demonstrate that CYDES has high algorithm selection quality for community detection in networks.