OpenRaiser/Intern-Atlas huggingface dataset:
Panel: Computational Methods about Knowledge Graph
Authors: Jie Tang, Jure Leskovec, Kuansan Wang, Deborah L. McGuinness, Hassan Sawaf
Venue: WWW
Year: 2019

This panel will focus on the cutting-edge computation methods, which can be applied to knowledge graph, such as latest NLP technologies to extract entities and relationships to build knowledge graphs, machine learning or deep learning methods on mining knowledge graph, and intelligent search or recommendations powered by knowledge graphs. Panelists are: Professor and the Vice Chair of the Department of Computer Science and Technology of Tsinghua University. I obtained my Ph.D. in DCST of Tsinghua University in 2006. My research interests include artificial intelligence, data mining, social networks, machine learning and knowledge graph, with an emphasis on designing new algorithms for mining social and knowledge networks. Associate Professor of Computer Science at Stanford University. My research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems I investigate are motivated by large scale data, the web and on-line media Managing Director of MSR Outreach, an organization with the mission to serve the research community. In addition to applying the intelligent technologies to make Bing and Cortana smarter in gathering and serving academic knowledge, we are also starting an experimental website, academic.microsoft.com (powered by Academic API), and mobile apps dedicated to exploring new service scenarios for active researchers like myself A leading expert in knowledge representation and reasoning languages and systems and has worked in ontology creation and evolution environments for over 20 years. Most recently, McGuinness is best known for her leadership role in semantic web research, and for her work on explanation, trust, and applications of semantic web technology, particularly for scientific applications. Director of Artificial Intelligence at Amazon Web Services, passionate about opening up new markets and opportunities with smart application of Artificial Intelligence and application-driven research in AI, in particular in language technology, speech processing, computer vision and computational reasoning.
Variational Session-based Recommendation Using Normalizing Flows
Authors: Fan Zhou, Zijing Wen, Kunpeng Zhang, Goce Trajcevski, Ting Zhong
Venue: WWW
Year: 2019

We present a novel generative Session-Based Recommendation (SBR) framework, called VAriational SEssion-based Recommendation (VASER) - a non-linear probabilistic methodology allowing Bayesian inference for flexible parameter estimation of sequential recommendations. Instead of directly applying extended Variational AutoEncoders (VAE) to SBR, the proposed method introduces normalizing flows to estimate the probabilistic posterior, which is more effective than the agnostic presumed prior approximation used in existing deep generative recommendation approaches. VASER explores soft attention mechanism to upweight the important clicks in a session. We empirically demonstrate that the proposed model significantly outperforms several state-of-the-art baselines, including the recently-proposed RNN/VAE-based approaches on real-world datasets.
Context-aware Variational Trajectory Encoding and Human Mobility Inference
Authors: Fan Zhou, Xiaoli Yue, Goce Trajcevski, Ting Zhong, Kunpeng Zhang
Venue: WWW
Year: 2019

Unveiling human mobility patterns is an important task for many downstream applications like point-of-interest (POI) recommendation and personalized trip planning. Compelling results exist in various sequential modeling methods and representation techniques. However, discovering and exploiting the context of trajectories in terms of abstract topics associated with the motion can provide a more comprehensive understanding of the dynamics of patterns. We propose a new paradigm for moving pattern mining based on learning trajectory context, and a method - Context-Aware Variational Trajectory Encoding and Human Mobility Inference (CATHI) - for learning user trajectory representation via a framework consisting of: (1) a variational encoder and a recurrent encoder; (2) a variational attention layer; (3) two decoders. We simultaneously tackle two subtasks: (T1) recovering user routes (trajectory reconstruction); and (T2) predicting the trip that the user would travel (trajectory prediction). We show that the encoded contextual trajectory vectors efficiently characterize the hierarchical mobility semantics, from which one can decode the implicit meanings of trajectories. We evaluate our method on several public datasets and demonstrate that the proposed CATHI can efficiently improve the performance of both subtasks, compared to state-of-the-art approaches.
Shop your Right Size: A System for Recommending Sizes for Fashion products
Authors: G. Mohammed Abdulla, Shreya Singh, Sumit Borar
Venue: WWW
Year: 2019

Size selection is a critical step while purchasing fashion products. Unlike offline, in online fashion shopping, customers don’t have the luxury of trying a product and have to rely on the product images and size charts to select a product that fits well. As a result of this gap, online shopping yields a large percentage of returns due to size and fit. Hence providing size recommendation for customers enhances their buying experience and also reduces operational costs incurred during exchanges and returns. In this paper, we present a robust personalized size recommendation system which predicts the most appropriate size for users based on their order history and product data. We embed both users and products in a size and fit space using skip-gram based Word2Vec model and employ GBM classifier to predict the fit likelihood. We describe the architecture of the system and challenges we encountered while developing it. Further we also analyze the performance of our system through extensive offline and online testing, compare our technique with another state-of-art technique and share our findings.
BotCamp: Bot-driven Interactions in Social Campaigns
Authors: Noor Abu-El-Rub, Abdullah Mueen
Venue: WWW
Year: 2019

Bots (i.e. automated accounts) involve in social campaigns typically for two obvious reasons: to inorganically sway public opinion and to build social capital exploiting the organic popularity of social campaigns. In the process, bots interact with each other and engage in human activities (e.g. likes, retweets, and following). In this work, we detect a large number of bots interested in politics. We perform multi-aspect (i.e. temporal, textual, and topographical) clustering of bots, and ensemble the clusters to identify campaigns of bots. We observe similarity among the bots in a campaign in various aspects such as temporal correlation, sentimental alignment, and topical grouping. However, we also discover bots compete in gaining attention from humans and occasionally engage in arguments. We classify such bot interactions in two primary groups: agreeing (i.e. positive) and disagreeing (i.e. negative) interactions and develop an automatic interaction classifier to discover novel interactions among bots participating in social campaigns.
The FAIR TRADE Framework for Assessing Decentralised Data Solutions
Authors: Maribel Acosta, Tim Berners-Lee, Anastasia Dimou, John Domingue, Luis-Daniel Ibáñez, Krzysztof Janowicz, Maria-Esther Vidal, Amrapali Zaveri
Venue: WWW
Year: 2019

Decentralised data solutions bring their own sets of capabilities, requirements and issues not necessarily present in centralised solutions. In order to compare the properties of different approaches or tools for management of decentralised data, it is important to have a common evaluation framework. We present a set of dimensions relevant to data management in decentralised contexts and use them to define principles extending the FAIR framework, initially developed for open research data. By characterising a range of different data solutions or approaches by how TRusted, Autonomous, Distributed and dEcentralised, in addition to how Findable, Accessible, Interoperable and Reusable, they are, we show that our FAIR TRADE framework is useful for describing and evaluating the management of decentralised data solutions, and aim to contribute to the development of best practice in a developing field.
Addressing Trust Bias for Unbiased Learning-to-Rank
Authors: Aman Agarwal, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork
Venue: WWW
Year: 2019

Existing unbiased learning-to-rank models use counterfactual inference, notably Inverse Propensity Scoring (IPS), to learn a ranking function from biased click data. They handle the click incompleteness bias, but usually assume that the clicks are noise-free, i.e., a clicked document is always assumed to be relevant. In this paper, we relax this unrealistic assumption and study click noise explicitly in the unbiased learning-to-rank setting. Specifically, we model the noise as the position-dependent trust bias and propose a noise-aware Position-Based Model, named TrustPBM, to better capture user click behavior. We propose an Expectation-Maximization algorithm to estimate both examination and trust bias from click data in TrustPBM. Furthermore, we show that it is difficult to use a pure IPS method to incorporate click noise and thus propose a novel method that combines a Bayes rule application with IPS for unbiased learning-to-rank. We evaluate our proposed methods on three personal search data sets and demonstrate that our proposed model can significantly outperform the existing unbiased learning-to-rank methods.
A Link-based Approach to Detect Media Bias in News Websites
Authors: Victória Patrícia Aires, Fabíola G. Nakamura, Eduardo Freire Nakamura
Venue: WWW
Year: 2019

News websites are currently one of the main sources of information. Like traditional media, these sources can have a bias in how they report news. This media bias can influence how people perceive events, political decisions, or discussions. In this paper, we describe a link-based approach to identify news websites with the same political orientation, i.e., characterize the bias of news websites, using network analysis techniques. After constructing a graph from a few seeds with previously known bias, we show that a community detection algorithm can identify groups formed by sources with the same political orientation.
Identifying High Value Opportunities for Human in the Loop Lexicon Expansion
Authors: Alfredo Alba, Chad DeLuca, Anna Lisa Gentile, Daniel Gruhl, Linda Kato, Chris Kau, Petar Ristoski, Steve Welch
Venue: WWW
Year: 2019

Many real world analytics problems examine multiple entities or classes that may appear in a corpus. For example, in a customer satisfaction survey analysis there are over 60 categories of (somewhat overlapping) concerns. Each of these is backed by a lexicon of terminology associated with the concern (e.g., “Easy, user friendly process” or ”Process confusing, too many handoffs”). These categories need to be expanded by a subject matter expert as the terminology is not always straight forward (e.g., “handoffs” may also include “ping-pong” and “hot potato” as relevant terms). But given that Subject Matter Expert time is costly, which of the 60+ lexicons should we expand first? We propose a metric for evaluating an existing set of lexicons and providing guidance on which are likely to benefit most from human-in-the-loop expansion. Using our ranking results we achieved ≈ 4 × improvement in impact when expanding the first few lexicons off our suggested list as compared to a random selection.
Task Oriented Data Exploration with Human-in-the-Loop. A Data Center Migration Use Case
Authors: Alfredo Alba, Chad DeLuca, Anna Lisa Gentile, Daniel Gruhl, Linda Kato, Chris Kau, Petar Ristoski, Steve Welch
Venue: WWW
Year: 2019

Data exploration is a task that inherently requires high human interaction. The subject matter expert looks at the data to identify a hypothesis, potential questions, and where to look for answers in the data. Virtually all data exploration scenarios can benefit from a tight human-in-the-loop paradigm, where data can be visualized and reshaped, but also augmented with missing semantic information - that the subject matter expert can supplement in itinere. In this demo we show a novel graph-based data exploration model where the subject matter expert can annotate and maneuver the data to answer specific questions. This demo specifically focuses on the task of migrating data centers, logically and/or physically, where the subject matter expert needs to identify the function of each node - a server, a virtual machine, a printer, etc - in the data center, which is not necessarily directly available in the data and to be able to plan a safe switch-off and relocation of a cluster of nodes. We show how the novel human-in-the-loop data exploration and enrichment paradigm helps designing the data center migration plan.
From Free-text User Reviews to Product Recommendation using Paragraph Vectors and Matrix Factorization
Authors: Georgios Alexandridis, Thanos Tagaris, Giorgos Siolas, Andreas Stafylopatis
Venue: WWW
Year: 2019

Recent theoretical and practical advances have led to the emergence of review-based recommender systems, where user preference data is encoded in at least two dimensions; the traditional rating scores in a predefined discrete scale and the user-generated reviews in the form of free-text. The main contribution of this work is the presentation of a new technique of incorporating those reviews into collaborative filtering matrix factorization algorithms. The text of each review, of arbitrary length, is mapped to a continuous feature space of fixed length, using neural language models and more specifically, the Paragraph Vector model. Subsequently, the resulting feature vectors (the neural embeddings) are used in combination with the rating scores in a hybrid probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. The proposed methodology is then compared to three other similar approaches on six datasets in order to assess its performance. The obtained results demonstrate that the new formulation outperforms the other systems on a set of two metrics, thereby indicating the robustness of the idea.
City-Wide Signal Strength Maps: Prediction with Random Forests
Authors: Emmanouil Alimpertis, Athina Markopoulou, Carter T. Butts, Konstantinos Psounis
Venue: WWW
Year: 2019

Signal strength maps are of great importance to cellular providers for network planning and operation, however they are expensive to obtain and possibly limited or inaccurate in some locations. In this paper, we develop a prediction framework based on random forests to improve signal strength maps from limited measurements. First, we propose a random forests (RFs)-based predictor, with a rich set of features including location as well as time, cell ID, device hardware and other features. We show that our RFs-based predictor can significantly improve the tradeoff between prediction error and number of measurements needed compared to state-of-the-art data-driven predictors, i.e., requiring 80% less measurements for the same prediction accuracy, or reduces the relative error by 17% for the same number of measurements. Second, we leverage two types of real-world LTE RSRP datasets to evaluate into the performance of different prediction methods: (i) a small but dense Campus dataset, collected on a university campus and (ii) several large but sparser NYC and LA datasets, provided by a mobile data analytics company.
Misinformation Dissemination on the Web
Authors: Jussara M. Almeida
Venue: WWW
Year: 2019

Misinformation dissemination is a topic that has gained a lot of attention from academia and public media, in general. Despite a rich literature on strategies to detect and mitigate this phenomenon, the problem still persists with impact on several sectors of the society. In this talk, I will discuss the problem, revise existing approaches as well as discuss challenges to properly address it. I will also discuss recent results of our group on the investigation of misinformation spread on WhatsApp.
Large Scale Human Sensing Over Time. Challenges and Lessons Learned
Authors: Omar Alonso
Venue: WWW
Year: 2019

Twitter and Facebook continue to be top destinations for information consumption on the Internet. The ever-expanding social graph based enables the implementation of traditional features like item recommendation and selection of trending content that rely on human input and other behavioral data. However, given the enormous amount of human sensing in the world at any given moment in any platform, there is a lot of untapped potential that goes beyond simple applications on top of atomic level content like a post or tweet. In this talk we describe a social knowledge graph that discover relationships as they occur over time and how it can be used to capture the evolution of events or stories.
The Practice of Labeling: Everything You Always Wanted to Know About Labeling (But Were Afraid to Ask)
Authors: Omar Alonso
Venue: WWW
Year: 2019

Many data intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. There are, however, practical issues with the adoption of human computation and crowdsourcing at scale in the real world. Building systems data processing pipelines that require crowd computing remains difficult. In this tutorial, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high quality labels.
Exploration of Product Search Intents via Clustering of Query Clusters
Authors: Omar Alonso, Vasileios Kandylas, Serge-Eric Tremblay
Venue: WWW
Year: 2019

We describe a system that organizes search results in the context of an exploratory product search session where the user is researching goods. Compared to existing approaches that use predefined categories to filter results by attributes, we organize information needs based on queries instead of documents. The idea is to organize queries around the same topic and produce a hierarchical representation of intents that describe information about a product from different perspectives. We present a prototype implementation using a real-world data set of 24M queries.
Open Personalized Navigation on the Sandbox of Wiki Pages
Authors: Chuankai An, Daniel N. Rockmore
Venue: WWW
Year: 2019

In this paper we present a proof-of-concept of a visual navigation tool for a personalized “sandbox” of Wiki pages. The navigation tool considers multiple groups of algorithmic parameters and adapts to user activity via graphical user interfaces. The output is a 2D map of a subset of Wikipedia pages network which provides a different and broader visual representation – a map – in the neighborhood (according to some metric) of the pages around the page currently displayed in a browser. The representation schema includes the incorporation of a kind of transparency in the algorithmic parameters affecting the presentation of the landscape visualization, which in turn enables the delivery of a personalized canvas, designed by the user. A case study shows the combination of four different sourcing (i.e., identification and extraction of the neighboring pages) rules and three layouts over the same Wikipedia subnetwork. The basic schema is readily adapted to other search experiences and contexts.
Longitudinal Adversarial Attack on Electronic Health Records Data
Authors: Sungtae An, Cao Xiao, Walter F. Stewart, Jimeng Sun
Venue: WWW
Year: 2019

Although deep learning models trained on electronic health records (EHR) data have shown state-of-the-art performance in many predictive clinical tasks, the discovery of adversarial examples (i.e., input data that are engineered to cause misclassification) has exposed vulnerabilities with lab and imaging data. We specifically consider adversarial examples with longitudinal EHR data, an area that has not been previously examined because of the challenges with temporal high-dimensional and sparse features. We propose Longitudinal AdVersarial Attack (, a saliency score based adversarial example using a method that requires a minimal number of perturbations and that automatically minimizes the likelihood of detection. Features are selected and modified by jointly modeling a saliency map and attention mechanism. Experimental results with longitudinal EHR data show that an substantially reduce model performance for attention-based target models (from AUPR = 0.5 to AUPR = 0.08).
Persuasive Technologies for Sustainable Smart Cities: The Case of Urban Mobility
Authors: Evangelia Anagnostopoulou, Babis Magoutas, Efthimios Bothos, Gregoris Mentzas
Venue: WWW
Year: 2019

In this paper, we study the effectiveness of personalized persuasive interventions to change urban travelers’ mobility behavior and nudge them towards more sustainable transport choices. More specifically, we embed a set of persuasive design elements in a route planning application and investigate how they affect users’ travel choices. The design elements take into consideration the style, the intensity, the target of persuasive interventions as well as users’ characteristics and the trip purpose. Our results show evidence that our proposed approach motivates users on a personal level to change their mobility behavior and make more sustainable choices. Furthermore, by personalizing the persuasive interventions while considering combinations of interventions styles (in our case messages and visualizations) as well as adjusting the intensity of persuasive interventions according to the trip purpose and the transport modes of the routes which the user is nudged to follow, the effects of the persuasive interventions can be increased.
RDF Graph Stores as Convergent Datatypes
Authors: James Anderson
Venue: WWW
Year: 2019

This report describes a way to represent and operate on an RDF dataset such the it behaves as an instance of a conflict free replicable datatype. In this industry presentation, we describe how we accomplish this for the Dydra RDF graph storage service in a manner compatible with the SPARQL Graph Store HTTP Protocol (GSP). The standard GSP concerns the current store state only. Dydra retains previous store states as active addressable aspects analogous to named graphs in a quad store. It incorporates and addresses arbitrary revisions of target datasets according to ETag and Content-Disposition specifications in HTTP headers. Appropriate interpretation of these arguments permits to replicate datasets among cooperating participants.
Characterizing the Behavioral Evolution of Twitter Users and The Truth Behind the 90-9-1 Rule
Authors: Alessia Antelmi, Delfina Malandrino, Vittorio Scarano
Venue: WWW
Year: 2019

Online Social Networks (OSNs) represent a fertile field to collect real user data and to explore OSNs user behavior. Recently, two topics are drawing the attention of researchers: the evolution of online social roles and the question of participation inequality. In this work, we bring these two fields together to study and characterize the behavioral evolution of OSNs users according to the quantity and the typology of their social interactions. We found that online participation on the microblogging platform can be categorized into four different activity levels. Furthermore, we empirically verified that the 90-9-1 rule of thumb about participation inequality is not an accurate representation of reality. Findings from our analysis reveal that lurkers are less than expected: they are not 9 out of 10 as suggested by Nielsen, but 3 out of 4. This represents a significant result that can give new insights on how users relate with social media and how their use is evolving towards a more active interaction with the new generation of consumers.