OpenRaiser/Intern-Atlas huggingface dataset:
Revenue monotone mechanisms for online advertising
Authors: Gagan Goel, Mohammad Reza Khani
Venue: WWW
Year: 2014

Online advertising is an essential part of the Internet and the main source of revenue for many web-centric firms such as search engines, social networks, and online publishers. A key component of online advertising is the auction mechanism which selects and prices the set of winning ads. This work is inspired by one of the biggest practical drawbacks of the widely popular Vickrey-Clarke-Groves (VCG) mechanism, which is the unique incentive-compatible mechanism that maximizes social welfare. It is known that VCG lacks a desired property of revenue monotonicity - a natural notion which states that the revenue of a mechanism shouldn't go down as the number of bidders increase or if the bidders increase their bids. Most firms which depend on online advertising revenue have a large sales team to attract more bidders on their inventory as the general belief is that more bidders will increase competition, and hence revenue. However, the lack of revenue monotonicity of VCG conflicts with this general belief and can be strategically confusing for the firm's business.
Allocating tasks to workers with matching constraints: truthful mechanisms for crowdsourcing markets
Authors: Gagan Goel, Afshin Nikzad, Adish Singla
Venue: WWW
Year: 2014

Designing optimal pricing policies and mechanisms for allocating tasks to workers is central to the online crowdsourcing markets. In this paper, we consider the following realistic setting of online crowdsourcing markets -- there is a requester with a limited budget and a heterogeneous set of tasks each requiring certain skills; there is a pool of workers and each worker has certain expertise and interests which define the set of tasks she can and is willing to do. Under the matching constraints given by this bipartite graph between workers and tasks, we design our incentive-compatible mechanism truthuniform which allocates the tasks to the workers, while ensuring budget feasibility and achieves near-optimal utility for the requester. Apart from strong theoretical guarantees, we carry out experiments on a realistic case study of Wikipedia translation project on Mechanical Turk. We note that this is the first paper to address this setting from a mechanism design perspective.
Contextual and dimensional relevance judgments for reusable SERP-level evaluation
Authors: Peter B. Golbus, Imed Zitouni, Jin Young Kim, Ahmed Hassan Awadallah, Fernando Diaz
Venue: WWW
Year: 2014

Document-level relevance judgments are a major component in the calculation of effectiveness metrics. Collecting high-quality judgments is therefore a critical step in information retrieval evaluation. However, the nature of and the assumptions underlying relevance judgment collection have not received much attention. In particular, relevance judgments are typically collected for each document in isolation, although users read each document in the context of other documents. In this work, we aim to investigate the nature of relevance judgment collection. We collect relevance labels in both isolated and conditional setting, and ask for judgments in various dimensions of relevance as well as overall relevance. Then we compare the relevance metrics based on various types of judgments with other metrics of quality such as user preference. Our analyses illuminate how these settings for judgment collection affect the quality and the characteristics of the judgments. We also find that the metrics based on conditional judgments show higher correlation with user preference than isolated judgments.
Optimal revenue-sharing double auctions with applications to ad exchanges
Authors: Renato Gomes, Vahab S. Mirrokni
Venue: WWW
Year: 2014

E-commerce web-sites such as Ebay as well as advertising exchanges (AdX) such as DoubleClick's, RightMedia, or AdECN work as intermediaries who sell items (e.g. page-views) on behalf of a seller (e.g. a publisher) to buyers on the opposite side of the market (e.g., advertisers). These platforms often use fixed-percentage sharing schemes, according to which (i) the platform runs an auction amongst buyers, and (ii) gives the seller a constant-fraction (e.g., 80%) of the auction proceeds. In these settings, the platform faces asymmetric information regarding both the valuations of buyers for the item (as in a standard auction environment) as well as about the seller's opportunity cost of selling the item. Moreover, platforms often face intense competition from similar market places, and such competition is likely to favor auction rules that secure high payoffs to sellers. In such an environment, what selling mechanism should platforms employ? Our goal in this paper is to study optimal mechanism design in settings plagued by competition and two-sided asymmetric information, and identify conditions under which the current practice of employing constant cuts is indeed optimal.
From graphs to tables the design of scalable systems for graph analytics
Authors: Joseph E. Gonzalez
Venue: WWW
Year: 2014

From social networks to language modeling, the growing scale and importance of graph data has driven the development of new graph-parallel systems. In this talk, I will review the graph-parallel abstraction and describe how it can be used to express important machine learning and graph analytics algorithms like PageRank and Latent factor models. I will present how systems like GraphLab and Pregel exploit restrictions in the graph-parallel abstraction along with advances in distributed graph representation to efficiently execute iterative graph algorithms orders of magnitude faster than more general data-parallel systems. Unfortunately, the same restrictions that enable graph-parallel systems to achieve substantial performance gains also limit their ability to express many of the important stages in a typical graph-analytics pipeline. As a consequence, existing approaches to graph-analytics typically compose multiple systems through brittle and costly file interfaces. To fill the need for a holistic approach to graph-analytics we introduce GraphX, which unifies graph-parallel and data-parallel computation under a single API and system. I will show how a simple set of data-parallel operators can be used to express graph-parallel computation and how, by applying a collection of query optimizations derived from our work on graph-parallel systems, we can execute entire graph-analytics pipelines efficiently in a more general data-parallel distributed fault-tolerant system achieving performance comparable to specialized state-of-the-art systems.
Enhancing web activities with information visualization
Authors: Eduardo Graells-Garrido
Venue: WWW
Year: 2014

Many activities people perform on the Web are biased, including activities like reading news, searching for information and connecting with people. Sometimes these biases are inherent in social behavior (like homophily), and sometimes they are external as they affect the system (like media bias). In this thesis proposal, we describe our approach to use information visualization to enhance Web activities performed by regular people (i.e., non-experts) We understand enhancing as reducing bias effects and generating an engaging response from users. Our methodology is based on case studies. We select a Web activity, identify the biases that affect it, and evaluate how the biases affect a population from online social networks using web mining techniques, and then, we design a visualization following an interactive and playful design approach to diminish the previously identified biases. We propose to evaluate the effect of our visualization designs in user studies by comparing them with state-of-the-art techniques considering a playful experiences framework.
Generating ad targeting rules using sparse principal component analysis with constraints
Authors: Mihajlo Grbovic, Slobodan Vucetic
Venue: WWW
Year: 2014

Determining the right audience for an advertising campaign is a well-established problem, of central importance to many Internet companies. Two distinct targeting approaches exist, the model-based approach, which leverages machine learning, and the rule-based approach, which relies on manual generation of targeting rules. Common rules include identifying users that had interactions (website visits, emails received, etc.) with the companies related to the advertiser, or search queries related to their product. We consider a problem of discovering such rules from data using Constrained Sparse PCA. The constraints are put in place to account for cases when evidence in data suggests a relation that is not appropriate for advertising. Experiments on real-world data indicate the potential of the proposed approach.
Tutoring from the desktop: facilitating learning through Google+ hangouts
Authors: Neel Guha
Venue: WWW
Year: 2014

Many studies have demonstrated the effectiveness of tutoring as a teaching strategy. Though much attention has recently been focussed on using the web to extend the reach of the university classroom to high achieving students, comparatively less attention has been paid to the potential of the web to bring personalized tutoring to at-risk students. In this paper, we describe Tutoring From the Desktop, a program in which high school students in California use Google Plus Hangouts to tutor students in Kohlapur, India. We show how a simple structured program can be used to over- come the barriers of time-zones, accents and much more.
Can web presence predict academic performance?: the case of Eötvös university
Authors: László Gulyás, Zsolt Jurányi, Sándor Soós, George Kampis
Venue: WWW
Year: 2014

This paper reports the preliminary results of a project that aims at incorporating the analysis of the web presence (content) of research institutions into the scientometric analysis of these research institutions. The problem is to understand and predict the dynamics of academic activity and resource allocation using web presence. The present paper approaches this problem in two parts. First we develop a crawler and an archive of the web contents obtained from academic institutions, and present an early analysis of the records. Second, we use (currently off-line records to analyze the dynamics of resource allocation. Combination of the two parts is an ambition of ongoing work. The motivation in this study is twofold. First, we strongly believe that independent archiving, indexing and searching of (past) web content is an important task, even with regards to academic web presence. We are particularly interested in studying the dynamics of the "online scientific discourse", based on the assumption that the changing traces of web presence is an important factor that documents the intensity of activity. Second, we maintain that the trend-analysis of scientific activity represents a hitherto unused potential. We illustrate this by a pilot where, using 'offline' longitudinal datasets, we study whether past (i.e. cumulative) success can predict current (and future) activity in academia. Or, in short: do institutions invest and publish in areas where they have been successful? Answer to this question is, we believe, important to understanding and predicting research policies and their changes.
Entity linking with a unified semantic representation
Authors: Zhaochen Guo, Denilson Barbosa
Venue: WWW
Year: 2014

Entity Linking (EL) consists in linking mentions in a document to their referent entities in a Knowledge Base. Current approaches fall into two main categories: local approaches, in which mentions are linked independently of each other, and global approaches, in which all mentions are linked collectively. Local approaches often ignore the semantic relatedness of entities, and while global approaches incorporate the semantic relatedness, they tend to focus only on directly connected entities, ignoring indirect connections which might be useful. We present a global EL approach that unifies the representation of the semantics of entities and documents--the probability distribution of entities being visited during a random walk on an entity graph--that accounts for direct and indirect connections. An experimental evaluation shows that our method outperforms five state-of-the-art EL systems and two very strong baselines.
Cross market modeling for query-entity matching
Authors: Manish Gupta, Prashant Borole, Praful Hebbar, Rupesh Kumar Mehta, Niranjan Nayak
Venue: WWW
Year: 2014

Given a query, the query-entity (QE) matching task involves identifying the best matching entity for the query. When modeling this task as a binary classification problem, two issues arise: (1) features in specific global markets (like de-at: German users in Austria) are quite sparse compared to other markets like en-us, and (2) training data is expensive to obtain in multiple markets and hence limited. Can we leverage some form of cross market data/features for effective query-entity matching in sparse markets? Our solution consists of three main modules: (1) Cross Market Training Data Leverage (CMTDL) (2) Cross Market Feature Leverage (CMFL), and (3) Cross Market Output Data Leverage (CMODL). Each of these parts perform "signal" sharing at different points during the classification process. Using a combination of these strategies, we show significant improvements in query-impression weighted coverage for the query-entity matching task.
Towards a social media analytics platform: event detection and user profiling for twitter
Authors: Manish Gupta, Rui Li, Kevin Chen-Chuan Chang
Venue: WWW
Year: 2014

Microblog data differs significantly from the traditional text data with respect to a variety of dimensions. Microblog data contains short documents, SMS kind of language, and is full of code mixing. Though a lot of it is mere social babble, it also contains fresh news coming from human sensors at a humungous rate. Given such interesting characteristics, the world wide web community has witnessed a large number of research tasks for microblogging platforms recently. Event detection on Twitter is one of the most popular such tasks with a large number of applications. The proposed tutorial on social analytics for Twitter will contain three parts. In the first part, we will discuss research efforts towards detection of events from Twitter using both the tweet content as well as other external sources. We will also discuss various applications for which event detection mechanisms have been put to use. Merely detecting events is not enough. Applications require that the detector must be able to provide a good description of the event as well. In the second part, we will focus on describing events using the best phrase, event type, event timespan, and credibility. In the third part, we will discuss user profiling for Twitter with a special focus on user location prediction. We will conclude with a summary and thoughts on future directions.
Tutorial on social recommender systems
Authors: Ido Guy
Venue: WWW
Year: 2014

In recent years, with the proliferation of the social web, users are exposed to an intensively growing social overload. Social recommender systems aim to address this overload and are becoming integral part of virtually any leading website, playing a key factor in its success. In this tutorial, we will review the broad domain of social recommender systems, the underlying techniques and methodologies; the data in use, recommended entities, and target population; evaluation techniques; applications; and open issues and challenges.
Constructed identity and social machines: a case study in creative media production
Authors: Amy Guy, Ewan Klein
Venue: WWW
Year: 2014

Current discussions of social machines rightly emphasise a human's role as a crucial part of a system rather than a user of a system. The human `parts' are typically considered in terms of their aggregate outcomes and collective behaviours, but human participants are rarely all equal, even within a small system. We argue that due to the complex nature of online identity, understanding participants in a more granular way is crucial for social machine observation and design. We present the results of a study of the personas portrayed by participants in a social machine that produces creative media content, and discover that inconsistent or misleading representations of individuals do not necessarily undermine the system in which they are participating. We describe a preliminary framework for making sense of human participants in social machines, and the ongoing work that develops this further.
Number frequency on the web
Authors: Willem Robert van Hage, Thomas Ploeger, Jesper Hoeksema
Venue: WWW
Year: 2014

In this article we investigate the properties of the frequency distribution of numbers on the Web. We work with a part of the Common Crawl dataset comprising 3.8 billion Web documents and a recent dump of the English language Wikipedia. We show that, like words, numbers on the Web follow a Power law distribution, and obey Benford's law of first-digits. We show and explain regularities in the distribution, and compare the regularities in Common Crawl to those in Wikipedia. The comparison stresses which patterns in the frequency distributions follow from human thought.
The W3C web cryptography API: motivation and overview
Authors: Harry Halpin
Venue: WWW
Year: 2014

The W3C Web Cryptography API is the standard API for accessing cryptographic primitives in Javascript-based environments. We describe the motivations behind the creation of the W3C Web Cryptography API and give a high-level overview with motivating use-cases while addressing objections.
The Berners-Lee hypothesis: power laws and group structure in flickr
Authors: Harry Halpin, Andrea Capocci
Venue: WWW
Year: 2014

An intriguing hypothesis, first suggested by Tim Berners-Lee, is that the structure of online groups should conform to a power law distribution. We believe this is likely a consequence of the Dunbar Number, which is a supposed limit to the number of persistent social contacts a user can have in a group. As preliminary results, we show that the number of contacts of a typical Flickr user, the number of groups a user belongs to, and the size of Flickr groups all follow power law distributions. Furthermore, we find some unexpected differences in the internal structure of public and private Flickr groups. For further research, we further operationalize the Berners-Lee hypothesis to suppose that users with a group membership distribu
XXXtortion?: inferring registration intent in the .XXX TLD
Authors: Tristan Halvorson, Kirill Levchenko, Stefan Savage, Geoffrey M. Voelker
Venue: WWW
Year: 2014

After a decade-long approval process, multiple rejections, and an independent review, ICANN approved the xxx TLD for inclusion in the Domain Name System, to begin general availability on December 6, 2011. Its sponsoring registry proposed it as an expansion of the name space, as well as a way to separate adult from child-appropriate content. Many independent groups, including trademark holders, political groups, and the adult entertainment industry itself, were concerned that it would primarily generate value through defensive and speculative registrations, without actually serving a real need. This paper measures the validity of these concerns using data gathered from ICANN, whois, and Web requests. We use this information to characterize each xxx domain and infer the registrant's most likely intent. We find that at most 3.8% of xxx domains host or redirect to potentially legitimate Web content, with the rest generally serving either defensive or speculative purposes. Indeed, registrants spent roughly $13M up front to defend existing brands and trademarks within the xxx TLD, and an additional $11M over the course of the first year. Additional evidence suggests that over 80% of annual domain registrations are for purely defensive purposes and do not even resolve.
Songrium: a music browsing assistance service with interactive visualization and exploration of protect a web of music
Authors: Masahiro Hamasaki, Masataka Goto, Tomoyasu Nakano
Venue: WWW
Year: 2014

This paper describes a music browsing assistance service, Songrium (http://songrium.jp), which increases user enjoyment when listening to songs and allows visualization and exploration of a ``Web of Music''. We define a Web of Music in this paper to be a network of ``web-native music'', which we define in turn to be music that is published, shared, and remixed (has derivative works created) entirely on the web. Songrium was developed as an attempt to realize a Web of Music, by showing relations between both original songs and derivative works and offering an enriched listening experience. Songrium has analyzed over 600,000 music video clips on the most popular Japanese video-sharing service, Niconico, which contains original songs of web-native music and their derivative works such as covers and dance arrangements. Analysis of over 100,000 original songs reveals that over 500,000 derivative works were generated and have contributed to enrich the Web of Music.
A voice-controlled web browser to navigate hierarchical hidden menus of web pages in a smart-tv environment
Authors: Sungjae Han, Geunseong Jung, Minsoo Ryu, Byung-Uk Choi, Jaehyuk Cha
Venue: WWW
Year: 2014

This paper proposes a new voice web browser that can be operated in smart TV environments. Previous voice web browsers had the limitation of being run under limited conditions; for example, a list of the specific contents of a page was outputted by voice, or the user entered a search term by voice. In our method proposed in this paper, all the hierarchical menu areas on a web page are recognized and controlled with voice keywords so that page navigation according to a menu can be conveniently done in a voice supported web browser. Although many studies have been conducted on web page menu recognition, most of them provide insufficient information to recognize the hierarchical menu structure. In other words, most web pages in recent browsers showed submenus only as a result of a specific user interaction, since these previous studies had no way of recognizing or controlling the submenus. Therefore, in the web browser proposed in this study, a hierarchical menu structure, which is inserted dynamically via user interaction, is recognized and selected by voice, thus making it possible to maneuver on the web page. Furthermore, the core code of the browser is implemented in JavaScript, so it can be flexibly used not only for a web browser on Smart TVs, but also as functional extensions of existing web browsers in a PC environment.
Characterizing user interest using heterogeneous media
Authors: Jonghyun Han, Hyunju Lee
Venue: WWW
Year: 2014

It is often hard to accurately estimate interests of social media users because their messages do not have additional information, such as a category. In this paper, we propose an approach that estimates user interest from social media to provide personalized services. Our approach employs heterogeneous media to map social media onto categories. To describe the categories, we propose a hybrid method that integrates a topic model with TF-ICF for extracting both explicitly presented and implicitly latent features. Our evaluation result shows that it gives the highest performance, compared to other approaches. Thus, we expect that the proposed approach is helpful in advancing personalization of social media services.