OpenRaiser/Intern-Atlas huggingface dataset:
Mobile search pattern evolution: the trend and the impact of voice queries
Authors: Jeonghee Yi, Farzin Maghoul
Venue: WWW
Year: 2011

In this paper we study the characteristics of search queries submitted from mobile devices using Yahoo! Search for Mobile during a 2 months period in early of 2010, and compare the results with a similar study conducted in late 2007. The major findings include 1) mobile search queries have become much more diverse, and 2) user interest and information needs have been substantially changed at least in some areas of search topics, including adult and local intent queries. In addition we investigate the impact of voice query search interface offered by Yahoo!'s mobile search service. We examine how unstructured spoken queries differ from conventional search queries.
On computing text-based similarity in scientific literature
Authors: Seok-Ho Yoon, Sang-Wook Kim, Ji-Soo Kim, Won-Seok Hwang
Venue: WWW
Year: 2011

This paper addresses computing of similarity among papers using text-based measures. First, we analyze the accuracy of the similarities computed using different parts of a paper, and propose a method of Keyword-Extension, which is very useful when text information is incomplete.
A distributed framework for reliable and efficient service choreographies
Authors: Young Yoon, Chunyang Ye, Hans-Arno Jacobsen
Venue: WWW
Year: 2011

In service-oriented architectures (SOA), independently developed Web services can be dynamically composed. However, the composition is prone to producing semantically conflicting interactions among the services. For example, in an interdepartmental business collaboration through Web services, the decision by the marketing department to clear out the inventory might be inconsistent with the decision by the operations department to increase production. Resolving semantic conflicts is challenging especially when services are loosely coupled and their interactions are not carefully governed. To address this problem, we propose a novel distributed service choreography framework. We deploy safety constraints to prevent conflicting behavior and enforce reliable and efficient service interactions via federated publish/subscribe messaging, along with strategic placement of distributed choreography agents and coordinators to minimize runtime overhead. Experimental results show that our framework prevents semantic conflicts with negligible overhead and scales better than a centralized approach by up to 60%.
Hierarchical organization of unstructured consumer reviews
Authors: Jianxing Yu, Zheng-Jun Zha, Meng Wang, Tat-Seng Chua
Venue: WWW
Year: 2011

In this paper, we propose to organize the aspects of a specific product into a hierarchy by simultaneously taking advantages of domain structure knowledge as well as consumer reviews. Based on the derived hierarchy, we generate a hierarchical organization of the consumer reviews based on various aspects of the product, and aggregate consumer opinions on the aspects. With such hierarchical organization, people can easily grasp the overview of consumer reviews and opinions on various aspects, as well as seek consumer reviews and opinions on any specific aspect by navigating through the hierarchy. We conduct evaluation on two product review data sets: Liu et al.'s data set containing 314 reviews for five products [2], and our review corpus which is collected from forum Web sites containing 60,786 reviews for five popular products. The experimental results demonstrate the effectiveness of our approach.
ARROW: GenerAting SignatuRes to Detect DRive-By DOWnloads
Authors: Junjie Zhang, Christian Seifert, Jack W. Stokes, Wenke Lee
Venue: WWW
Year: 2011

A drive-by download attack occurs when a user visits a webpage which attempts to automatically download malware without the user's consent. Attackers sometimes use a malware distribution network (MDN) to manage a large number of malicious webpages, exploits, and malware executables. In this paper, we provide a new method to determine these MDNs from the secondary URLs and redirect chains recorded by a high-interaction client honeypot. In addition, we propose a novel drive-by download detection method. Instead of depending on the malicious content used by previous methods, our algorithm first identifies and then leverages the URLs of the MDN's central servers, where a central server is a common server shared by a large percentage of the drive-by download attacks in the same MDN. A set of regular expression-based signatures are then generated based on the URLs of each central server. This method allows additional malicious webpages to be identified which launched but failed to execute a successful drive-by download attack. The new drive-by detection system named ARROW has been implemented, and we provide a large-scale evaluation on the output of a production drive-by detection system. The experimental results demonstrate the effectiveness of our method, where the detection coverage has been boosted by 96% with an extremely low false positive rate.
The freshman handbook: a hint for the server placement of social networks
Authors: Ying Zhang, Mallik Tatipamula
Venue: WWW
Year: 2011

There has been a recent unprecedented increase in the use of Online Social Networks (OSNs) to expand our social life, exchange information and share common interests. Many popular OSNs today attract hundreds of millions of users who share tremendous amount of data on it such as Facebook, Twitter, and Buzz. Given the huge business opportunities OSNs may bring, more and more new social applications has emerged on the Internet. For these newcomers in the social network business, one of the first key decisions to make is to where to deploy the computational resources to best accommodate future client requests. In this work, we aim at providing useful suggests to the new born social network providers (freshman) on the intelligent server placement, by exploring available public information from existing social network communities. In this work, we first propose three scalable server placement strategies for OSNs. Our solution can scalably select server locations among all the possible locations, at the same time reducing the cost for inter-user data sharing.
Statically locating web application bugs caused by asynchronous calls
Authors: Yunhui Zheng, Tao Bao, Xiangyu Zhang
Venue: WWW
Year: 2011

Ajaxbecomesmoreandmoreimportantforwebapplications that care about client side user experience. It allows sending requests asynchronously, without blocking clients from continuing execution. Callback functions are only executed upon receiving the responses. While such mechanism makes browsing a smooth experience, it may cause severe problems in the presence of unexpected network latency, due to the non-determinism of asynchronism. In this paper, we demonstrate the possible problems caused by the asynchronism and propose a static program analysis to automatically detect such bugs in web applications. As client side Ajax code is often wrapped in server-side scripts, we also develop a technique that extracts client-side JavaScript code from server-side scripts. We evaluate our technique on a number of real-world web applications. Our results show that it can effectively identify real bugs. We also discuss possible ways to avoid such bugs.
Leveraging auxiliary text terms for automatic image annotation
Authors: Ning Zhou, Yi Shen, Jinye Peng, Xiaoyi Feng, Jianping Fan
Venue: WWW
Year: 2011

This paper proposes a novel algorithm to annotate web images by automatically aligning the images with their most relevant auxiliary text terms. First, the DOM-based web page segmentation is performed to extract images and their most relevant auxiliary text blocks. Second, automatic image clustering is used to partition the web images into a set of groups according to their visual similarity contexts, which significantly reduces the uncertainty on the relatedness between the images and their auxiliary terms. The semantics of the visually-similar images in the same cluster are then described by the same ranked list of terms which frequently co-occur in their text blocks. Finally, a relevance re-ranking process is performed over a term correlation network to further refine the ranked term list. Our experiments on a large-scale database of web pages have provided very positive results.
A unified framework for recommending diverse and relevant queries
Authors: Xiaofei Zhu, Jiafeng Guo, Xueqi Cheng, Pan Du, Huawei Shen
Venue: WWW
Year: 2011

Query recommendation has been considered as an effective way to help search users in their information seeking activities. Traditional approaches mainly focused on recommending alternative queries with close search intent to the original query. However, to only take relevance into account may generate redundant recommendations to users. It is better to provide diverse as well as relevant query recommendations, so that we can cover multiple potential search intents of users and minimize the risk that users will not be satisfied. Besides, previous query recommendation approaches mostly relied on measuring the relevance or similarity between queries in the Euclidean space. However, there is no convincing evidence that the query space is Euclidean. It is more natural and reasonable to assume that the query space is a manifold. In this paper, therefore, we aim to recommend diverse and relevant queries based on the intrinsic query manifold. We propose a unified model, named manifold ranking with stop points, for query recommendation. By turning ranked queries into stop points on the query manifold, our approach can generate query recommendations by simultaneously considering both diversity and relevance in a unified way. Empirical experimental results on a large scale query log of a commercial search engine show that our approach can effectively generate highly diverse as well as closely related query recommendations.
WAI-ACT: web accessibility now
Authors: Shadi Abou-Zahra
Venue: WWW
Year: 2012

The W3C web accessibility standards have now existed for over a decade yet implementation of accessible websites, software, and web technologies is lagging behind this development. This fact is largely due to lack of knowledge and expertise among developers and due to fragmentation of web accessibility approaches. It is an opportune time to develop authoritative practical guidance and harmonized approaches, and to research potential challenges and opportunities in future technologies in a collaborative setting. The EC-funded WAI-ACT project addresses these needs through use of an open cooperation framework that builds on and extends the existing mechanisms of the W3C Web Accessibility Initiative (WAI). This paper presents the WAI-ACT project and how it will drive accessibility implementation in advanced web technologies.
Learning from users' querying experience on intranets
Authors: Ibrahim Adepoju Adeyanju, Dawei Song, M-Dyaa Albakour, Udo Kruschwitz, Anne N. De Roeck, Maria Fasli
Venue: WWW
Year: 2012

Query recommendation is becoming a common feature of web search engines especially those for Intranets where the context is more restrictive. This is because of its utility for supporting users to find relevant information in less time by using the most suitable query terms. Selection of queries for recommendation is typically done by mining web documents or search logs of previous users. We propose the integration of these approaches by combining two models namely the concept hierarchy, typically built from an Intranet's documents, and the query flow graph, typically built from search logs. However, we build our concept hierarchy model from terms extracted from a subset (training set) of search logs since these are more representative of the user view of the domain than any concepts extracted from the collection. We then continually adapt the model by incorporating query refinements from another subset (test set) of the user search logs. This process implies learning from or reusing previous users' querying experience to recommend queries for a new but similar user query. The adaptation weights are extracted from a query flow graph built with the same logs. We evaluated our hybrid model using documents crawled from the Intranet of an academic institution and its search logs. The hybrid model was then compared to a concept hierarchy model and query flow graph built from the same collection and search logs respectively. We also tested various strategies for combining information in the search logs with respect to the frequency of clicked documents after query refinement. Our hybrid model significantly outperformed the concept hierarchy model and query flow graph when tested over two different periods of the academic year. We intend to further validate our experiments with documents and search logs from another institution and devise better strategies for selecting queries for recommendation from the hybrid model.
Associating structured records to text documents
Authors: Rakesh Agrawal, Ariel Fuxman, Anitha Kannan, John C. Shafer, Partha Pratim Talukdar
Venue: WWW
Year: 2012

Postulate two independently created data sources. The first contains text documents, each discussing one or a small number of objects. The second is a collection of structured records, each containing information about the characteristics of some objects. We present techniques for associating structured records to corresponding text documents and empirical results supporting the proposed techniques.
Textual and contextual patterns for sentiment analysis over microblogs
Authors: Fotis Aisopos, George Papadakis, Konstantinos Tserpes, Theodora A. Varvarigou
Venue: WWW
Year: 2012

Microblog content poses serious challenges to the applicability of sentiment analysis, due to its inherent characteristics. We introduce a novel method relying on content-based and context-based features, guaranteeing high effectiveness and robustness in the settings we are considering. The evaluation of our methods over a large Twitter data set indicates significant improvements over the traditional techniques.
Build your own music recommender by modeling internet radio streams
Authors: Natalie Aizenberg, Yehuda Koren, Oren Somekh
Venue: WWW
Year: 2012

In the Internet music scene, where recommendation technology is key for navigating huge collections, large market players enjoy a considerable advantage. Accessing a wider pool of user feedback leads to an increasingly more accurate analysis of user tastes, effectively creating a "rich get richer" effect. This work aims at significantly lowering the entry barrier for creating music recommenders, through a paradigm coupling a public data source and a new collaborative filtering (CF) model. We claim that Internet radio stations form a readily available resource of abundant fresh human signals on music through their playlists, which are essentially cohesive sets of related tracks. In a way, our models rely on the knowledge of a diverse group of experts in lieu of the commonly used wisdom of crowds. Over several weeks, we aggregated publicly available playlists of thousands of Internet radio stations, resulting in a dataset encompassing millions of plays, and hundreds of thousands of tracks and artists. This provides the large scale ground data necessary to mitigate the cold start problem of new items at both mature and emerging services.
Web intelligence and communities
Authors: Rajendra Akerkar, Pierre Maret, Laurent Vercouter
Venue: WWW
Year: 2012

Web Intelligence deals with intelligent methods and information & communication technologies that are integrated to enhance different web-based applications. Communities are popular, particularly on the World Wide Web, as a means for like-minded individuals to pursue common goals. Communities appear as a first-class object in the areas of web intelligence and agent technologies, as well as a crucial crossroads of several subdomains (i.e. user modelling, protocols, data management, data mining, content modelling, etc.). These sub-domains impact the nature of the communities and the applications which are related to them. In this editorial, the use of Web Intelligence and communities is discussed together with ways in which a wide range of research is benefiting this area for the long-term. Also the WI&C workshop's goal and structure are introduced.
GLOCAL: event-based retrieval of networked media
Authors: Pierre Andrews, Francesco G. B. De Natale, Sven Buschbeck, Anthony Jameson, Kerstin Bischoff, Claudiu S. Firan, Claudia Niederée, Vasileios Mezaris, Spiros Nikolopoulos, Vanessa Murdock, Adam Rae
Venue: WWW
Year: 2012

The idea of the European project GLOCAL is to use events as the central concept for search, organization and combination of multimedia content from various sources. For this purpose methods for event detection and event matching as well as media analysis are developed. Considered events range from private, over local, to global events.
PAC'nPost: a framework for a micro-blogging social network in an unstructured P2P network
Authors: H. Asthana, Ingemar J. Cox
Venue: WWW
Year: 2012

We describe a framework for a micro-blogging social network implemented in an unstructured peer-to-peer network. A micro-blogging social network must provide capabilities for users to (i) publish, (ii) follow and (iii) search. Our retrieval mechanism is based on a probably approximately correct (PAC) search architecture in which a query is sent to a fixed number of nodes in the network. In PAC, the probability of attaining a particular accuracy is a function of the number of nodes queried (fixed) and the replication rate of documents (micro-blog). Publishing a micro-blog then becomes a matter of replicating the micro-blog to the required number of random nodes without any central coordination. To solve this, we use techniques from the field of rumour spreading (gossip protocols) to propagate new documents. Our document spreading algorithm is designed such that a document has a very high probability of being copied to only the required number of nodes. Results from simulations performed on networks of 10,000, 100,000 and 500,000 nodes verify our mathematical models. The framework is also applicable for indexing dynamic web pages in a distributed search engine or for a system which indexes newly created BitTorrents in a decentralized environment.
SWiPE: searching wikipedia by example
Authors: Maurizio Atzori, Carlo Zaniolo
Venue: WWW
Year: 2012

A novel method is demonstrated that allows semantic and well-structured knowledge bases (such as DBpedia) to be easily queried directly from Wikipedia's pages. Using Swipe, naive users with no knowledge of RDF triples and SPARQL can easily query DBpedia with powerful questions such as: "Who are the U.S. presidents who took office when they were 55-year old or younger, during the last 60 years", or "Find the town in California with less than 10 thousand people". This is accomplished by a novel Search by Example (SBE) approach where a user can enter the query conditions directly on the Infobox of a Wikipedia page. In fact, Swipe activates various fields of Wikipedia to allow users to enter query conditions, and then uses these conditions to generate equivalent SPARQL queries and execute them on DBpedia. Finally, Swipe returns the query results in a form that is conducive to query refinements and further explorations. Swipe's SBE approach makes semi-structured documents queryable in an intuitive and user-friendly way and, through Wikipedia, delivers the benefits of querying and exploring large knowledge bases to all Web users.
Outage detection via real-time social stream analysis: leveraging the power of online complaints
Authors: Eriq Augustine, Cailin Cushing, Alex Dekhtyar, Kevin McEntee, Kimberly Paterson, Matt Tognetti
Venue: WWW
Year: 2012

Over the past couple of years, Netflix has significantly expanded its online streaming offerings, which now encompass multiple delivery platforms and thousands of titles available for instant view. This paper documents the design and development of an outage detection system for the online services provided by Netflix. Unlike other internal quality-control measures used at Netflix, this system uses only publicly available information: the tweets, or Twitter posts, that mention the word "Netflix," and has been developed and deployed externally, on servers independent of the Netflix infrastructure. This paper discussed the system and provides assessment of the accuracy of its real-time detection and alert mechanisms.
Impact of ad impressions on dynamic commercial actions: value attribution in marketing campaigns
Authors: Joel Barajas, Ram Akella, Marius Holtan, Jaimie Kwon, Aaron Flores, Victor Andrei
Venue: WWW
Year: 2012

We develop a descriptive method to estimate the impact of ad impressions on commercial actions dynamically without tracking cookies. We analyze 2,885 campaigns for 1,251 products from the Advertising.com ad network. We compare our method with A/B testing for 2 campaigns, and with a public synthetic dataset.
From linked data to linked entities: a migration path
Authors: Giovanni Bartolomeo, Stefano Salsano
Venue: WWW
Year: 2012

Entities have been deserved special attention in the latest years, however their identification is still troublesome. Existing approaches exploit ad hoc services or centralized architectures. In this paper we present a novel approach to recognize naturally emerging entity identifiers built on top of Linked Data concepts and protocols.
Audience dynamics of online catch up TV
Authors: Thomas Beauvisage, Jean-Samuel Beuscart
Venue: WWW
Year: 2012

This paper studies the demand for TV contents on online catch up platforms, in order to assess how catch up TV offers transform TV consumption. We build upon empirical data on French TV consumption in June 2011: a daily monitoring of online audience on web catch up platforms, and live audience ratings of traditional broadcast TV. We provide three main results: 1) online consumption is more concentrated than off-line audience, contradicting the hypothesis of a long tail effect of catch up TV; 2) the temporality of replay TV consumption on the web is very close to the live broadcasting of the programs, thus softening rather than breaking the synchrony of traditional TV; 3) detailed data on online consumption of news reveals two patterns of consumption ("alternative TV ritual" vs. "à la carte").
Targeting online communities to maximise information diffusion
Authors: Václav Belák, Samantha Lam, Conor Hayes
Venue: WWW
Year: 2012

In recent years, many companies have started to utilise online social communities as a means of communicating with and targeting their employees and customers. Such online communities include discussion fora which are driven by the conversational activity of users. For example, users may respond to certain ideas as a result of the influence of their neighbours in the underlying social network. We analyse such influence to target communities rather than individual actors because information is usually shared with the community and not just with individual users. In this paper, we study information diffusion across communities and argue that some communities are more suitable for maximising spread than others. In order to achieve this, we develop a set of novel measures for cross-community influence, and show that it outperforms other targeting strategies on 51 weeks of data of the largest Irish online discussion system, Boards.ie.