OpenRaiser/Intern-Atlas huggingface dataset:
Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games
Authors: Lucas Augusto Montalvão Costa Carvalho, Hendrik Teixeira Macedo
Venue: WWW
Year: 2013

A major difficulty in a recommendation system for groups is to use a group aggregation strategy to ensure, among other things, the maximization of the average satisfaction of group members. This paper presents an approach based on the theory of noncooperative games to solve this problem. While group members can be seen as game players, the items for potential recommendation for the group comprise the set of possible actions. Achieving group satisfaction as a whole becomes, then, a problem of finding the Nash equilibrium. Experiments with a MovieLens dataset and a function of arithmetic mean to compute the prediction of group satisfaction for the generated recommendation have shown statistically significant results when compared to state-of-the-art aggregation strategies, in particular, when evaluation among group members are more heterogeneous. The feasibility of this unique approach is shown by the development of an application for Facebook, which recommends movies to groups of friends.
Adaptive crowdsourcing for temporal crowds
Authors: L. Elisa Celis, Koustuv Dasgupta, Vaibhav Rajan
Venue: WWW
Year: 2013

Crowdsourcing is rapidly emerging as a computing paradigm that can employ the collective intelligence of a distributed human population to solve a wide variety of tasks. However, unlike organizational environments where workers have set work hours, known skill sets and performance indicators that can be monitored and controlled, most crowdsourcing platforms leverage the capabilities of fleeting workers who exhibit changing work patterns, expertise, and quality of work. Consequently, platforms exhibit significant variability in terms of performance characteristics (like response time, accuracy, and completion rate). While this variability has been folklore in the crowdsourcing community, we are the first to show data that displays this kind of changing behavior. Notably, these changes are not due to a distribution with high variance; rather, the distribution itself is changing over time. Deciding which platform is most suitable given the requirements of a task is of critical importance in order to optimize performance; further, making the decision(s) adaptively to accommodate the dynamically changing crowd characteristics is a problem that has largely been ignored. In this paper, we address the changing crowds problem and, specifically, propose a multi-armed bandit based framework. We introduce the simple epsilon-smart algorithm that performs robustly. Counterfactual results based on real-life data from two popular crowd platforms demonstrate the efficacy of the proposed approach. Further simulations using a random-walk model for crowd performance demonstrate its scalability and adaptability to more general scenarios.
Is this app safe for children?: a comparison study of maturity ratings on Android and iOS applications
Authors: Ying Chen, Heng Xu, Yilu Zhou, Sencun Zhu
Venue: WWW
Year: 2013

There is a rising concern among parents who have experienced unreliable content maturity ratings for mobile applications (apps) that result in inappropriate risk exposure for their children and adolescents. In reality, there is no consistent maturity rating policy for mobile applications. The maturity ratings of Android apps are provided purely by developers' self-disclosure and are rarely verified. While Apple's iOS app ratings are considered to be more accurate, they can also be inconsistent with Apple's published policies. To address these issues, this research aims to systematically uncover the extent and severity of unreliable maturity ratings for mobile apps. Specifically, we develop mechanisms to verify the maturity ratings of mobile apps and investigate possible reasons behind the incorrect ratings. We believe that our findings have important implications for platform providers (e.g., Google or Apple) as well as for regulatory bodies and application developers.
Preserving user privacy from third-party applications in online social networks
Authors: Yuan Cheng, Jaehong Park, Ravi S. Sandhu
Venue: WWW
Year: 2013

Online social networks (OSNs) facilitate many third-party applications (TPAs) that offer users additional functionality and services. However, they also pose serious user privacy risk as current OSNs provide little control over disclosure of user data to TPAs. Addressing the privacy and security issues related to TPAs (and the underlying social networking platforms) requires solutions beyond a simple all-or-nothing strategy. In this paper, we outline an access control framework that provides users flexible controls over how TPAs can access user data and activities in OSNs while still retaining the functionality of TPAs. The proposed framework specifically allows TPAs to utilize some private data without actually transmitting this data to TPAs. Our approach determines access from TPAs based on user-specified policies in terms of relationships between the user and the application.
Optimization of ads allocation in sponsored search
Authors: Alexey Ya. Chervonenkis, Anna N. Sorokina, Valery A. Topinsky
Venue: WWW
Year: 2013

We introduce the optimization problem of target-specific ads allocation. Technique for solving this problem for different target-constraints structures is presented. This technique allows us to find optimal ads allocation which maximize the target such as CTR, Revenue or other system performances subject to some linear constraints. We show that the optimal ads allocation depends on both the target and constraints variables.
Complementary assistance mechanisms for end user mashup composition
Authors: Soudip Roy Chowdhury, Olexiy Chudnovskyy, Matthias Niederhausen, Stefan Pietschmann, Paul Sharples, Florian Daniel, Martin Gaedke
Venue: WWW
Year: 2013

Despite several efforts for simplifying the composition process, learning efforts required for using existing mashup editors to develop mashups remain still high. In this paper, we describe how this barrier can be lowered by means of an assisted development approach that seamlessly integrates automatic composition and interactive pattern recommendation techniques into existing mashup platforms for supporting easy mashup development by end users. We showcase the use of such an assisted development environment in the context of an open-source mashup platform Apache Rave. Results of our user studies demonstrate the benefits of our approach for end user mashup development.
MASFA: mass-collaborative faceted search for online communities
Authors: Seth B. Cleveland, Byron J. Gao
Venue: WWW
Year: 2013

Faceted search combines faceted navigation with direct keyword search, providing exploratory search capacities allowing progressive query refinement. It has become the de facto standard for e-commerce and product-related websites such as amazon.com and ebay.com. However, faceted search has not been effectively incorporated into non-commercial online community portals such as craigslist.org. This is mainly because unlike keyword search, faceted search systems require metadata that constantly evolve, making them very costly to build and maintain. In this paper, we propose a framework MASFA that utilizes a set of non-domain-specific techniques to build and maintain effective, portable, and cost-free faceted search systems in a mass-collaborative manner. We have implemented and deployed the framework on selected categories of Craigslist to demonstrate its utility.
Recommending collaborators using keywords
Authors: Sara Cohen, Lior Ebel
Venue: WWW
Year: 2013

This paper studies the problem of recommending collaborators in a social network, given a set of keywords. Formally, given a query q, consisting of a researcher s (who is a member of a social network) and a set of keywords k (e.g., an article name or topic of future work), the collaborator recommendation problem is to return a high-quality ranked list of possible collaborators for s on the topic k. Extensive effort was expended to define ranking functions that take into consideration a variety of properties, including structural proximity to s, textual relevance to k, and importance. The effectiveness of our methods have been experimentally proven over two large subsets of the social network determined by DBLP co-authorship data. The results show that the ranking methods developed in this paper work well in practice.
How to grow more pairs: suggesting review targets for comparison-friendly review ecosystems
Authors: James Cook, Alex Fabrikant, Avinatan Hassidim
Venue: WWW
Year: 2013

We consider the algorithmic challenges behind a novel interface that simplifies consumer research of online reviews by surfacing relevant comparable review bundles: reviews for two or more of the items being researched, all generated in similar enough circumstances to provide for easy comparison. This can be reviews by the same reviewer, or by the same demographic category of reviewer, or reviews focusing on the same aspect of the items. But such an interface will work only if the review ecosystem often has comparable review bundles for common research tasks.
Group chats on Twitter
Authors: James Cook, Krishnaram Kenthapadi, Nina Mishra
Venue: WWW
Year: 2013

We report on a new kind of group conversation on Twitter that we call a group chat. These chats are periodic, synchronized group conversations focused on specific topics and they exist at a massive scale. The groups and the members of these groups are not explicitly known. Rather, members agree on a hashtag and a meeting time (e.g, 3pm Pacific Time every Wednesday) to discuss a subject of interest. Topics of these chats are numerous and varied. Some are support groups, for example, post-partum depression and mood disorder groups. Others are about a passionate interest: topics include skiing, photography, movies, wine and foodie communities. We develop a definition of a group that is inspired by how sociologists define groups and present an algorithm for discovering groups. We prove that our algorithms find all groups under certain assumptions. While these groups are of course known to the people who participate in the discussions, what we do not believe is known is the scale and variety of groups. We provide some insight into the nature of these groups based on over two years of tweets. Finally, we show that group chats are a growing phenomenon on Twitter and hope that reporting their existence propels their growth even further.
ClausIE: clause-based open information extraction
Authors: Luciano Del Corro, Rainer Gemulla
Venue: WWW
Year: 2013

We propose ClausIE, a novel, clause-based approach to open information extraction, which extracts relations and their arguments from natural language text. ClausIE fundamentally differs from previous approaches in that it separates the detection of ``useful'' pieces of information expressed in a sentence from their representation in terms of extractions. In more detail, ClausIE exploits linguistic knowledge about the grammar of the English language to first detect clauses in an input sentence and to subsequently identify the type of each clause according to the grammatical function of its constituents. Based on this information, ClausIE is able to generate high-precision extractions; the representation of these extractions can be flexibly customized to the underlying application. ClausIE is based on dependency parsing and a small set of domain-independent lexica, operates sentence by sentence without any post-processing, and requires no training data (whether labeled or unlabeled). Our experimental study on various real-world datasets suggests that ClausIE obtains higher recall and higher precision than existing approaches, both on high-quality text as well as on noisy text as found in the web.
A survey of web archive search architectures
Authors: Miguel Costa, Daniel Gomes, Francisco M. Couto, Mário J. Silva
Venue: WWW
Year: 2013

Web archives already hold more than 282 billion documents and users demand full-text search to explore this historical information. This survey provides an overview of web archive search architectures designed for time-travel search, i.e. full-text search on the web within a user-specified time interval. Performance, scalability and ease of management are important aspects to take in consideration when choosing a system architecture. We compare these aspects and initialize the discussion of which search architecture is more suitable for a large-scale web archive.
A framework for learning web wrappers from the crowd
Authors: Valter Crescenzi, Paolo Merialdo, Disheng Qiu
Venue: WWW
Year: 2013

The development of solutions to scale the extraction of data from Web sources is still a challenging issue. High accuracy can be achieved by supervised approaches but the costs of training data, i.e., annotations over a set of sample pages, limit their scalability. Crowd sourcing platforms are making the manual annotation process more affordable. However, the tasks demanded to these platforms should be extremely simple, to be performed by non-expert people, and their number should be minimized, to contain the costs. We introduce a framework to support a supervised wrapper inference system with training data generated by the crowd. Training data are labeled values generated by means of membership queries, the simplest form of queries, posed to the crowd. We show that the costs of producing the training data are strongly affected by the expressiveness of the wrapper formalism and by the choice of the training set. Traditional supervised wrapper inference approaches use a statically defined formalism, assuming it is able to express the wrapper. Conversely, we present an inference algorithm that dynamically chooses the expressiveness of the wrapper formalism and actively selects the training set, while minimizing the number of membership queries to the crowd. We report the results of experiments on real web sources to confirm the effectiveness and the feasibility of the approach.
ALFRED: crowd assisted data extraction
Authors: Valter Crescenzi, Paolo Merialdo, Disheng Qiu
Venue: WWW
Year: 2013

The development of solutions to scale the extraction of data from Web sources is still a challenging issue. High accuracy can be achieved by supervised approaches, but the costs of training data, i.e., annotations over a set of sample pages, limit their scalability. Crowdsourcing platforms are making the manual annotation process more affordable. However, the tasks demanded to these platforms should be extremely simple, to be performed by non-expert people, and their number should be minimized, to contain the costs. We demonstrate ALFRED, a wrapper inference system supervised by the workers of a crowdsourcing platform. Training data are labeled values generated by means of membership queries, the simplest form of queries, posed to the crowd. ALFRED includes several original features: it automatically selects a representative sample set from the input collection of pages; in order to minimize the wrapper inference costs, it dynamically sets the expressiveness of the wrapper formalism and it adopts an active learning algorithm to select the queries posed to the crowd; it is able to manage inaccurate answers that can be provided by the workers engaged by crowdsourcing platforms.
Lightweight server support for browser-based CSRF protection
Authors: Alexei Czeskis, Alexander Moshchuk, Tadayoshi Kohno, Helen J. Wang
Venue: WWW
Year: 2013

Cross-Site Request Forgery (CSRF) attacks are one of the top threats on the web today. These attacks exploit ambient authority in browsers (eg cookies, HTTP authentication state), turning them into confused deputies and causing undesired side effects on vulnerable web sites. Existing defenses against CSRFs fall short in their coverage and/or ease of deployment. In this paper, we present a browser/server solution, Allowed Referrer Lists (ARLs), that addresses the root cause of CSRFs and removes ambient authority for participating web sites that want to be resilient to CSRF attacks. Our solution is easy for web sites to adopt and does not affect any functionality on non-participating sites. We have implemented our design in Firefox and have evaluated it with real-world sites. We found that ARLs successfully block CSRF attacks, are simpler to implement than existing defenses, and do not significantly impact browser performance.
MicroFilter: real time filtering of microblogging content
Authors: Ryadh Dahimene, Cédric du Mouza
Venue: WWW
Year: 2013

Microblogging systems have become a major trend over the Web. After only 7 years of existence, Twitter for instance claims more than 500 million users with more than 350 billion delivered update each day. As a consequence the user must today manage possibly extremely large feeds, resulting in poor data readability and loss of valuable information and the system must face a huge network load. In this demonstration, we present and illustrate the features of MicroFilter (MF in the the following), an inverted list-based filtering engine that nicely extends existing centralized microblogging systems by adding a real-time filtering feature. The demonstration proposed illustrates how the user experience is improved, the impact on the traffic for the overall system, and how the characteristics of microblogs drove the design of the indexing structures.
Pseudonymity in social machines
Authors: Ben Dalton
Venue: WWW
Year: 2013

This paper describes the potential of systems in which many people collectively control a single constructed identity mediated by socio-technical networks. By looking to examples of identities that have spontaneously emerged from anonymous communities online, a model for pseudonym design in social machines is proposed. A framework of identity dimensions is presented as a means of exploring the functional types of identity encountered in social machines, and design guidelines are outlined that suggest possible approaches to this task.
Learning from quizzes using intelligent learning companions
Authors: Danica Damljanovic, David Miller, Daniel O&apos, Sullivan
Venue: WWW
Year: 2013

It is widely recognised that engaging games can have a profound impact on learning. Integrating a conversational Artificial Intelligence (AI) into the mix makes the experience of learning even more engaging and enriching. In this paper we describe a conversational agent which is built with the purpose of acting as a personal tutor. The tutor can prompt, question, stimulate and guide a learner and then adapt exercises and challenges to specific needs. We illustrate how automatic generation of quizzes can be used to build learning exercises and activities.
Aggregating information from the crowd and the network
Authors: Anirban Dasgupta
Venue: WWW
Year: 2013

In social systems, information often exists in a dispersed manner, as individual opinions, local insights and preferences. In order to make a global decision however, we need to be able to aggregate such local pieces of information into a global description of the system. Such information aggregation problems are key in setting up crowdsourcing or human computation systems. How do we formally build and analyze such information aggregation systems? In this talk we will discuss three different vignettes based on the particular information aggregation problem and the "social system" that we are extracting the information from.
Timespent based models for predicting user retention
Authors: Kushal S. Dave, Vishal Vaingankar, Sumanth Kolar, Vasudeva Varma
Venue: WWW
Year: 2013

Content discovery is fast becoming the preferred tool for user engagement on the web. Discovery allows users to get educated and entertained about their topics of interest. StumbleUpon is the largest personalized content discovery engine on the Web, delivering more than 1 billion personalized recommendations per month. As a recommendation system one of the primary metrics we track is whether the user returns (retention) to use the product after their initial experience (session) with StumbleUpon.
A first view of exedra: a domain-specific language for large graph analytics workflows
Authors: Miyuru Dayarathna, Toyotaro Suzumura
Venue: WWW
Year: 2013

In recent years, many programming models, software libraries, and middleware have appeared for processing large graphs of various forms. However, there exists a significant usability gap between the graph analysis scientists, and High Performance Computing (HPC) application programmers due to the complexity of HPC graph analysis software. In this paper we provide a basic view of Exedra, a domain-specific language (DSL) for large graph analysis in which we aim to eliminate the aforementioned complexities. Exedra consists of high level language constructs for specifying different graph analysis tasks on distributed environments. We implemented Exedra DSL on a scalable graph analysis platform called Dipper. Dipper uses Igraph/R interface for creating graph analysis workflows which in turn gets translated to Exedra statements. Exedra statements are interpreted by Dipper interpreter, and gets mapped to user specified libraries/middleware. Exedra DSL allows for synthesize of graph algorithms that are more efficient compared to bare use of graph libraries while maintaining a standard interface that could use even future graph analysis software. We evaluated Exedra's feasibility for expressing graph analysis tasks by running Dipper on a cluster of four nodes. We observed that Dipper has the ability of reducing the time taken for graph analysis when the workflow was distributed on all four nodes despite the communication, and data format conversion overhead of the Dipper framework.
Understanding and analysing microblogs
Authors: Pinar Yanardag Delul
Venue: WWW
Year: 2013

Microblogging is a form of blogging where posts typically consist of short content such as quick comments, phrases, URLs, or media, like images and videos. Because of the fast and compact nature of microblogs, users have adopted them for novel purposes, including sharing personal updates, spreading breaking news, promoting political views, marketing and tracking real time events. Thus, finding relevant information sources out of the rapidly growing content is an essential task.
Living analytics methods for the web observatory
Authors: Ernesto Diaz-Aviles
Venue: WWW
Year: 2013

The collective effervescence of social media production has been enjoying a great deal of success in recent years. The hundred of millions of users who are actively participating in the Social Web are exposed to ever-growing amounts of sites, relationships, and information.
Towards real-time collaborative filtering for big fast data
Authors: Ernesto Diaz-Aviles, Wolfgang Nejdl, Lucas Drumond, Lars Schmidt-Thieme
Venue: WWW
Year: 2013

The Web of people is highly dynamic and the life experiences between our on-line and "real-world" interactions are increasingly interconnected. For example, users engaged in the Social Web more and more rely upon continuous social streams for real-time access to information and fresh knowledge about current affairs. However, given the deluge of data items, it is a challenge for individuals to find relevant and appropriately ranked information at the right time. Having Twitter as test bed, we tackle this information overload problem by following an online collaborative approach. That is, we go beyond the general perspective of information finding in Twitter, that asks: "What is happening right now?", towards an individual user perspective, and ask: "What is interesting to me right now within the social media stream?". In this paper, we review our recently proposed online collaborative filtering algorithms and outline potential research directions.