OpenRaiser/Intern-Atlas huggingface dataset:
Exploriometer: Leveraging Personality Traits for Coverage and Diversity Aware Recommendations
Authors: Evangelos Chatzicharalampous, Christos Zigkolis, Athena Vakali
Venue: WWW
Year: 2015

Since the first introduced Collaborative Filtering Recommenders (CFR) there have been many attempts to improve their performance by enhancing the prediction accuracy. Even though rating prediction is the prevailing paradigm in CFR, there are other issues which have gained significant attention with respect to the content and its variety. Coverage, which constitutes the degree to which recommendations cover the set of available items, is an important factor along with diversity of the items proposed to an individual, often measured by an average dissimilarity between all pairs of recommended items. In this paper, we argue that coverage and diversity cannot be effectively addressed by conventional CFR with pure similarity-based neighborhood creation processes, especially in sparse datasets. Motivated by the need for including wider content characteristics, we propose a novel neighbor selection technique which emphasizes on variety in preferences (to cover polyphony in selection). Our approach consists of a new metric, named "Exploriometer", which acts as a personality trait for users based on their rating behavior. We favor users who are explorers in order to increase polyphony, and subsequently coverage and diversity; but we still select similar users when we create neighborhoods as a solid basis in order to keep accuracy levels high. The proposed approach has been experimented by two real-world datasets (MovieLens and Yahoo! Music ) with coverage, diversity and accuracy aware recommendations extracted by both traditional CFR and CFR enhanced with our neighborhood creation process. We also introduce a new metric, inspired by the Pearson Correlation Coefficient, to estimate the diversity of recommended items. The derived results demonstrate that our neighbor selection technique can enhance coverage and diversity of the recommendations, especially on sparse datasets.
DiagramFlyer: A Search Engine for Data-Driven Diagrams
Authors: Zhe Chen, Michael J. Cafarella, Eytan Adar
Venue: WWW
Year: 2015

A large amount of data is available only through data-driven diagrams such as bar charts and scatterplots. These diagrams are stylized mixtures of graphics and text and are the result of complicated data-centric production pipelines. Unfortunately, neither text nor image search engines exploit these diagram-specific properties, making it difficult for users to find relevant diagrams in a large corpus. In response, we propose DiagramFlyer, a search engine for finding data-driven diagrams on the web. By recovering the semantic roles of diagram components (e.g., axes, labels, etc.), we provide faceted indexing and retrieval for various statistical diagrams. A unique feature of DiagramFlyer is that it is able to "expand" queries to include not only exactly matching diagrams, but also diagrams that are likely to be related in terms of their production pipelines. We demonstrate the resulting search system by indexing over 300k images pulled from over 150k PDF documents.
Online Learning to Rank: Absolute vs. Relative
Authors: Yiwei Chen, Katja Hofmann
Venue: WWW
Year: 2015

Online learning to rank holds great promise for learning personalized search result rankings. First algorithms have been proposed, namely absolute feedback approaches, based on contextual bandits learning; and relative feedback approaches, based on gradient methods and inferred preferences between complete result rankings. Both types of approaches have shown promise, but they have not previously been compared to each other. It is therefore unclear which type of approach is the most suitable for which online learning to rank problems. In this work we present the first empirical comparison of absolute and relative online learning to rank approaches.
AVER: Random Walk Based Academic Venue Recommendation
Authors: Zhen Chen, Feng Xia, Huizhen Jiang, Haifeng Liu, Jun Zhang
Venue: WWW
Year: 2015

Academic venues act as the main platform of communities in academia and the bridge of connecting researchers, which have rapidly developed in recent years. However, information overload in big scholarly data creates tremendous challenges for mining useful and effective information in order to recommend researchers to acknowledge high quality and fruitful academic venues, thereby enabling them to participate in relevant academic conferences as well as contributing to important/influential journals. In this work, we propose AVER, a novel random walk based Academic VEnue Recommendation model. AVER runs a random walk with restart model on a co-publication network which contains two kinds of associations, coauthor relations and author-venue relations. Moreover, we define a transfer matrix with bias to drive the random walk by exploiting three academic factors, co-publication frequency, weight of relations and researchers' academic level. AVER is inspired from the fact that researchers are more likely to contact those who have high co-publication frequency and similar academic levels. Additionally, in AVER, we consider the difference of weights between two kinds of associations. We conduct extensive experiments on DBLP data set in order to evaluate the performance of AVER. The results demonstrate that, in comparison to relevant baseline approaches, AVER performs better in terms of precision, recall and F1.
Semantic Tagging of Mathematical Expressions
Authors: Pao-Yu Chien, Pu-Jen Cheng
Venue: WWW
Year: 2015

Semantic tagging of mathematical expressions (STME) gives semantic meanings to tokens in mathematical expressions. In this work, we propose a novel STME approach that relies on neither text along with expressions, nor labelled training data. Instead, our method only requires a mathematical grammar set. We point out that, besides the grammar of mathematics, the special property of variables and user habits of writing expressions help us understand the implicit intents of the user. We build a system that considers both restrictions from the grammar and variable properties, and then apply an unsupervised method to our probabilistic model to learn the user habits. To evaluate our system, we build large-scale training and test datasets automatically from a public math forum. The results demonstrate the significant improvement of our method, compared to the maximum-frequency baseline. We also create statistics to reveal the properties of mathematics language.
Smith Search: Opinion-Based Restaurant Search Engine
Authors: Jaehoon Choi, Donghyeon Kim, Donghee Choi, Sangrak Lim, Seongsoon Kim, Jaewoo Kang, Youngjae Choi
Venue: WWW
Year: 2015

Search engines have become an important decision-making tool today. Unfortunately, they still need to improve in answering complex queries. The answers to complex decision-making queries such as ``best burgers and fries'' and ``good restaurants for anniversary dinner,'' are often subjective. The most relevant answer to the query can be obtained by only collecting people's opinions about the query, which are expressed in various venues on the Web. Collected opinions are converted into a ``consensus'' list. All of this should be processed at query time, which is impossible under the current search paradigm. To address this problem, we introduce Smith, a novel opinion-based restaurant search engine. Smith actively processes opinions on the Web, blogs, review boards, and other forms of social media at index time, and produces consensus answers from opinions at query time. The Smith search app (iOS) is available for download at http://www.smithsearches.com/introduction/.
An Architecture for Information Extraction from Figures in Digital Libraries
Authors: Sagnik Ray Choudhury, Clyde Lee Giles
Venue: WWW
Year: 2015

Scholarly documents contain multiple figures representing experimental findings. These figures are generated from data which is not reported anywhere else in the paper. We propose a modular architecture for analyzing such figures. Our architecture consists of the following modules: 1. An extractor for figures and associated metadata (figure captions and mentions) from PDF documents; 2. A Search engine on the extracted figures and metadata; 3. An image processing module for automated data extraction from the figures and 4. A natural language processing module to understand the semantics of the figure. We discuss the challenges in each step, report an extractor algorithm to extract vector graphics from scholarly documents and a classification algorithm for figures. Our extractor algorithm improves the state of the art by more than 10% and the classification process is very scalable, yet achieves 85\% accuracy. We also describe a semi-automatic system for data extraction from figures which is integrated with our search engine to improve user experience.
Collaborative Ranking with a Push at the Top
Authors: Konstantina Christakopoulou, Arindam Banerjee
Venue: WWW
Year: 2015

The goal of collaborative filtering is to get accurate recommendations at the top of the list for a set of users. From such a perspective, collaborative ranking based formulations with suitable ranking loss functions are natural. While recent literature has explored the idea based on objective functions such as NDCG or Average Precision, such objectives are difficult to optimize directly. In this paper, building on recent advances from the learning to rank literature, we introduce a novel family of collaborative ranking algorithms which focus on accuracy at the top of the list for each user while learning the ranking functions collaboratively. We consider three specific formulations, based on collaborative p-norm push, infinite push, and reverse-height push, and propose efficient optimization methods for learning these models. Experimental results illustrate the value of collaborative ranking, and show that the proposed methods are competitive, usually better than existing popular approaches to personalized recommendation.
Mouse Clicks Can Recognize Web Page Visitors!
Authors: Daniela Chudá, Peter Krátky, Jozef Tvarozek
Venue: WWW
Year: 2015

Behavioral biometrics based on mouse usage can be used to recognize one's identity, with special applications in anonymous Web browsing. Out of many features that describe browsing behavior, mouse clicks (or touches) as the most basic of navigation actions, provide a stable stream of behavioral data. The paper describes a method to recognize Web user according to three click features. The distance-based classification comparing cumulative distribution functions achieves high recognition accuracy even with hundreds of users.
Adaptive Sequential Experimentation Techniques for A/B Testing and Model Tuning
Authors: Scott Clark
Venue: WWW
Year: 2015

We introduce Bayesian Global Optimization as an efficient way to optimize a system's parameters, when evaluating parameters is time-consuming or expensive. The adaptive sequential experimentation techniques described can be used to help tackle a myriad of problems including optimizing a system's click-through or conversion rate via online A/B testing, tuning parameters of a machine learning prediction method or expensive batch job, designing an engineering system or finding the optimal parameters of a real-world physical experiment. We explore different tools available for performing these tasks, including Yelp's MOE and SigOpt. We will present the motivation, implementation, and background of these tools. Applications and examples from industry and best practices for using the techniques will be provided.
A Smart City Data Model based on Semantics Best Practice and Principles
Authors: Sergio Consoli, Misael Mongiovì, Andrea Giovanni Nuzzolese, Silvio Peroni, Valentina Presutti, Diego Reforgiato Recupero, Daria Spampinato
Venue: WWW
Year: 2015

Data management is crucial in modern smart cities. A good data model for smart cities has to be able to describe and integrate data from multiple domains, such as geographic information, public transportation, road maintenance, waste collection, and urban faults management. We describe our approach for creating a semantic platform for the Municipality of Catania, one of the main cities in Southern Italy. The ultimate goal is to boost the metropolis towards the route of a modern smart city and improve urban life. Our platform exhibits a consistent, minimal and comprehensive semantic data model for the city based on the Linked Open Data paradigm. Both the model and the data are publically accessible thorough dedicated user-friendly services, which allow citizens to observe and interact with the work of the public administration. Our platform also enables interested businesses and programmers to develop front-end services on the top of it. We describe the methodology used to extract data from sources, enrich them, building an ontology that describes them and publish them under the Linked Open Data paradigm. We include in our description employed tools and technologies. Our methodology is based on the standards of the W3C, on good practices of ontology design, on the guidelines issued by the Agency for Digital Italy and the Italian Index of Public Administration, as well as on the in-depth experience of the researchers in this field.
An Urban Fault Reporting and Management Platform for Smart Cities
Authors: Sergio Consoli, Diego Reforgiato Recupero, Misael Mongiovì, Valentina Presutti, Gianni Cataldi, Wladimiro Patatu
Venue: WWW
Year: 2015

A good interaction between public administrations and citizens is imperative in modern smart cities. Semantic web technologies can aid in achieving such a goal. We present a smart urban fault reporting web platform to help citizens in reporting common urban problems, such as street faults, potholes or broken street lights, and to support the local public administration in responding and fixing those problems quickly. The tool is based on a semantic data model designed for the city, which integrates several distinct data sources, opportunely re-engineered to meet the principles of the Semantic Web and linked open data. The platform supports the whole process of road maintenance, from the fault reporting to the management of maintenance activities. The integration of multiple data sources enables increasing interoperability and heterogeneous information retrieval, thus favoring the development of effective smart urban fault reporting services. Our platform was evaluated in a real case study: a complete urban reporting and road maintenance system has been developed for the municipality of Catania. Our approach is completely generalizable and can be adopted by and customized for other cities. The final goal is to stimulate smart maintenance services in the "cities of the future".
Geo Data Annotator: a Web Framework for Collaborative Annotation of Geographical Datasets
Authors: Stefano Cresci, Davide Gazzè, Angelica Lo Duca, Andrea Marchetti, Maurizio Tesconi
Venue: WWW
Year: 2015

In this paper we illustrate the Geo Data Annotator (GDA), a framework which helps a user to build a ground-truth dataset, starting from two separate geographical datasets. GDA exploits two kinds of indices to ease the task of manual annotation: geographical-based and string-based. GDA provides also a mechanism to evaluate the quality of the built ground-truth dataset. This is achieved through a collaborative platform, which allows many users to work to the same project. The quality evaluation is based on annotator agreement, which exploits the Fleiss' kappa statistic.
A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages
Authors: Stefano Cresci, Maurizio Tesconi, Andrea Cimino, Felice Dell&apos, Orletta
Venue: WWW
Year: 2015

This work focuses on the analysis of Italian social media messages for disaster management and aims at the detection of messages carrying critical information for the damage assessment task. A main novelty of this study consists in the focus on out-domain and cross-event damage detection, and on the investigation of the most relevant tweet-derived features for these tasks. We devised different experiments by resorting to a wide set of linguistic features qualifying the lexical and grammatical structure of a text as well as ad-hoc features specifically implemented for this task. We investigated the most effective features that allow to achieve the best results. A further result of this study is the construction of the first manually annotated Italian corpus of social media messages for damage assessment.
TopChurn: Maximum Entropy Churn Prediction Using Topic Models Over Heterogeneous Signals
Authors: Manirupa Das, Micha Elsner, Arnab Nandi, Rajiv Ramnath
Venue: WWW
Year: 2015

With the onset of social media and news aggregators on the Web, the newspaper industry is faced with a declining subscriber base. In order to retain customers both on-line and in print, it is therefore critical to predict and mitigate customer churn. Newspapers typically have heterogeneous sources of valuable data: circulation data, customer subscription information, news content, and search click log data. An ensemble of predictive models over multiple sources faces unique challenges -- ascertaining short-term versus long-term effects of features on churn, and determining mutual information properties across multiple data sources. We present TopChurn, a novel system that uses topic models as a means of extracting dominant features from user complaints and Web data for churn prediction. TopChurn uses a maximum entropy-based approach to identify features that are most indicative of subscribers likely to drop subscription within a specified period of time. We conduct temporal analyses to determine long-term versus short-term effects of status changes on subscriber accounts, included in our temporal models of churn; and topic and sentiment analyses on news and clicklogs, included in our Web models of churn. We then validate our insights via experiments over real data from The Columbus Dispatch, a mainstream daily newspaper, and demonstrate that our churn models significantly outperform baselines for various prediction windows.
Discovering the Rise and Fall of Software Engineering Ideas from Scholarly Publication Data
Authors: Subhajit Datta, Santonu Sarkar, A. S. M. Sajeev, Nishant Kumar
Venue: WWW
Year: 2015

For researchers and practitioners of a relatively young discipline like software engineering, an enduring concern is to identify the acorns that will grow into oaks -- ideas remaining most current in the long run. Additionally, it is interesting to know how the ideas have risen in importance, and fallen, perhaps to rise again. We analyzed a corpus of 19,000+ papers written by 21,000+ authors across 16 software engineering publication venues from 1975 to 2010, to empirically determine the half-life of software engineering research topics. We adapted existing measures of half-life as well as defined a specific measure based on publication and citation counts. The results from this empirical study are a presented in this paper.
A Two-Iteration Clustering Method to Reveal Unique and Hidden Characteristics of Items Based on Text Reviews
Authors: Alon Dayan, Osnat Mokryn, Tsvi Kuflik
Venue: WWW
Year: 2015

This paper presents a new method for extracting unique features of items based on their textual reviews. The method is built of two similar iterations of applying a weighting scheme and then clustering the resultant set of vectors. In the first iteration, restaurants of similar food genres are grouped together into clusters. The second iteration reduces the importance of common terms in each such cluster, and highlights those that are unique to each specific restaurant. Clustering the restaurants again, now according to their unique features, reveals very interesting connections between the restaurants.
Online View Maintenance for Continuous Query Evaluation
Authors: Soheila Dehghanzadeh, Alessandra Mileo, Daniele Dell&apos, Aglio, Emanuele Della Valle, Shen Gao, Abraham Bernstein
Venue: WWW
Year: 2015

In Web stream processing, there are queries that integrate Web data of various velocity, categorized broadly as streaming (i.e., fast changing) and background (i.e., slow changing) data. The introduction of local views on the background data speeds up the query answering process, but requires maintenance processes to keep the replicated data up-to-date. In this work, we study the problem of maintaining local views in a Web setting, where background data are usually stored remotely, are exposed through services with constraints on the data access (e.g., invocation rate limits and data access patterns) and, contrary to the database setting, do not provide streams with changes over their content. Then, we propose an initial solution: WBM, a method to maintain the content of the view with regards to query and user-defined constraints on accuracy and responsiveness.
Network-based Origin Confusion Attacks against HTTPS Virtual Hosting
Authors: Antoine Delignat-Lavaud, Karthikeyan Bhargavan
Venue: WWW
Year: 2015

We investigate current deployment practices for virtual hosting, a widely used method for serving multiple HTTP and HTTPS origins from the same server, in popular content delivery networks, cloud-hosting infrastructures, and web servers. Our study uncovers a new class of HTTPS origin confusion attacks: when two virtual hosts use the same TLS certificate, or share a TLS session cache or ticket encryption key, a network attacker may cause a page from one of them to be loaded under the other's origin in a client browser. These attacks appear when HTTPS servers are configured to allow virtual host fallback from a client-requested, secure origin to some other unexpected, less-secure origin. We present evidence that such vulnerable virtual host configurations are widespread, even on the most popular and security-scrutinized websites, thus allowing a network adversary to hijack pages, or steal secure cookies and single sign-on tokens. To prevent our virtual host confusion attacks and recover the isolation guarantees that are commonly assumed in shared hosting environments, we propose fixes to web server software and advocate conservative configuration guidelines for the composition of HTTP with TLS.
Objective Bayesian Two Sample Hypothesis Testing for Online Controlled Experiments
Authors: Alex Deng
Venue: WWW
Year: 2015

As A/B testing gains wider adoption in the industry, more people begin to realize the limitations of the traditional frequentist null hypothesis statistical testing (NHST). The large number of search results for the query "Bayesian A/B testing" shows just how much the interest in the Bayesian perspective is growing. In recent years there are also voices arguing that Bayesian A/B testing should replace frequentist NHST and is strictly superior in all aspects. Our goal here is to clarify the myth by looking at both advantages and issues of Bayesian methods. In particular, we propose an objective Bayesian A/B testing framework for which we hope to bring the best from Bayesian and frequentist methods together. Unlike traditional methods, this method requires the existence of historical A/B test data to objectively learn a prior. We have successfully applied this method to Bing, using thousands of experiments to establish the priors.