OpenRaiser/Intern-Atlas huggingface dataset:
Estimating the web robot population
Authors: Yang Sun 0002, C. Lee Giles
Venue: WWW
Year: 2010

In this research, capture-recapture (CR) models are used to estimate the population of web robots based on web server access logs from different websites. Each robot is considered as an individual randomly surfing the web and each website is considered as a trap that records the visitation of robots. We use maximum likelihood estimator to fit the observation data. Results show that there are 3,860 identifiable robot User-Agent strings and 780,760 IP addresses being used by web robots around the world. We also examine the origination of the named robots by their IP addresses. The results suggest that over 50% of web robot IP addresses are from United States and China.
Visualizing differences in web search algorithms using the expected weighted hoeffding distance
Authors: Mingxuan Sun 0001, Guy Lebanon, Kevyn Collins-Thompson
Venue: WWW
Year: 2010

We introduce a new dissimilarity function for ranked lists, the expected weighted Hoeffding distance, that has several advantages over current dissimilarity measures for ranked search results. First, it is easily customized for users who pay varying degrees of attention to websites at different ranks. Second, unlike existing measures such as generalized Kendall's tau, it is based on a true metric, preserving meaningful embeddings when visualization techniques like multi-dimensional scaling are applied. Third, our measure can effectively handle partial or missing rank information while retaining a probabilistic interpretation. Finally, the measure can be made computationally tractable and we give a highly efficient algorithm for computing it. We then apply our new metric with multi-dimensional scaling to visualize and explore relationships between the result sets from different search engines, showing how the weighted Hoeffding distance can distinguish important differences in search engine behavior that are not apparent with other rank-distance metrics. Such visualizations are highly effective at summarizing and analyzing insights on which search engines to use, what search strategies users can employ, and how search results evolve over time. We demonstrate our techniques using a collection of popular search engines, a representative set of queries, and frequently used query manipulation methods.
Alhambra: a system for creating, enforcing, and testing browser security policies
Authors: Shuo Tang, Chris Grier, Onur Aciiçmez, Samuel T. King
Venue: WWW
Year: 2010

Alhambra is a browser-based system designed to enforce and test web browser security policies. At the core of Alhambra is a policy-enhanced browser supporting fine-grain security policies that restrict web page contents and execution. Alhambra requires no server-side modifications or additions to the web application. Policies can restrict the construction of the document as well as the execution of JavaScript using access control rules and a taint-tracking engine. Using the Alhambra browser, we present two security policies that we have built using our architecture, both designed to prevent cross-site scripting. The first policy uses a taint-tracking engine to prevent cross-site scripting attacks that exploit bugs in the client-side of the web applications. The second one uses browsing history to create policies that restrict the contents of documents and prevent the inclusion of malicious content.
Keyword search over key-value stores
Authors: Arash Termehchy, Marianne Winslett
Venue: WWW
Year: 2010

Key-value stores (KVSs) are the most prevalent storage systems for large scale web services. As they do not have the structural complexities of RDBMSs, they are more efficient. In this paper, we introduce the problem of keyword search over KVSs and analyze its differences with keyword search over documents and relational databases. We propose a novel method called Keyword Search over Key-value stores (KSTORE) to solve the problem. Our user study using two real life data sets shows that KSTORE provides better ranking than the extended versions of document and relational database keyword search proposals and poses reasonable overheads.
Video search: are algorithms all we need?
Authors: Jeff Ubois, Jamie Davidson, Marko Grobelnik, Paul Over, Hans Westerhof
Venue: WWW
Year: 2010

This panel will debate various approaches to improving video search, and explore how professional cataloguing, crowd sourced metadata, and improvements in search algorithms will evolve over the next ten years. Panelists will explore the needs of large scale video archives, and compare these against the current capabilities of video search.
A practical system for harvesting and monitoring hot topics on the web
Authors: Xiaojun Wan 0001, Jianwu Yang
Venue: WWW
Year: 2010

This poster briefly describes a practical system named FounderWISE for harvesting and monitoring hot topics on the Web. FounderWISE consists of five components: Web crawler, text classifier, topic detector, topic summarizer and topic analyzer. In this poster we present two key components of topic detector and topic analyzer. The system has been successfully deployed in a few Chinese major government departments.
Analyzing content-level properties of the web adversphere
Authors: Yong Wang, Daniel Burgener, Aleksandar Kuzmanovic, Gabriel Maciá-Fernández
Venue: WWW
Year: 2010

Advertising has become an integral and inseparable part of the World Wide Web. However, neither public auditing nor monitoring mechanisms still exist in this emerging area. In this paper, we present our initial efforts on building a content-level auditing service for web-based ad networks. Our content-level measurements - understanding the ad distribution mechanisms and evaluating location-based and behavioral targeting approaches - bring useful auditing information to all entities involved in the online advertising business. We extensively evaluate Google's, AOL's, and Adblade's ad networks and demonstrate how their different design philosophies dominantly affect their performance at the content level.
MindFinder: image search by interactive sketching and tagging
Authors: Changhu Wang, Zhiwei Li 0006, Lei Zhang 0001
Venue: WWW
Year: 2010

In this technical demonstration, we showcase the MindFinder system $-$ a novel image search engine. Different from existing interactive image search engines, most of which only provide image-level relevance feedback, MindFinder enables users to sketch and tag query images at object level. By considering the image database as a huge repository, MindFinder is able to help users present and refine their initial thoughts in their mind, and finally turn thoughts to a beautiful image(s). Multiple actions are enabled for users to flexibly design their queries in a bilateral interactive manner by leveraging the whole image database, including tagging, refining query by dragging and dropping objects from search results, as well as editing objects. After each action, the search results will be updated in real time to provide users up-to-date materials to further formulate the query. By the deliberate but easy design of the query, MindFinder not only tries to enable users to present on the query panel whatever they are imagining, but also returns to users the most similar images to the picture in users' mind. By scaling up the image database to 10 million, MindFinder has the potential to reveal whatever in users' mind, that is where the name MindFinder comes from.
Co-optimization of multiple relevance metrics in web search
Authors: Dong Wang 0022, Chenguang Zhu, Weizhu Chen, Gang Wang 0010, Zheng Chen 0001
Venue: WWW
Year: 2010

Several relevance metrics, such as NDCG, precision and pSkip, are proposed to measure search relevance, where different metrics try to characterize search relevance from different perspectives. Yet we empirically find that the direct optimization of one metric cannot always achieve the optimal ranking of another metric. In this paper, we propose two novel relevance optimization approaches, which take different metrics into a global consideration where the objective is to achieve an ideal tradeoff between different metrics. To achieve this objective, we propose to co-optimize multiple relevance metrics and show their effectiveness.
FormSys: form-processing web services
Authors: Ingo M. Weber, Hye-Young Paik, Boualem Benatallah, Zifei Gong, Liangliang Zheng, Corren Vorwerk
Venue: WWW
Year: 2010

In this paper we present FormSys, a Web-based system that service-enables form documents. It offers two main services: filling in forms based on Web services' incoming SOAP messages, and invoking Web services based on filled-in forms. This can be applied to benefit individuals to reduce the number of often repetitive form fields they have to complete manually in many scenarios. It can also help organisations to remove the need for manual data entry by automatically triggering business process implementations based on incoming case data from filled-in forms. While the concept applies to forms of any type of document, our implementation uses Adobe AcroForms due to its universal applicability, availability of a usable API, and end-user appeal. In the demo, we will show the two core functions, namely soap2pdf and pdf2soap, along with use case applications of the services developed from real world scenarios. Essentially, this work demonstrates how PDFs can be used as a channel for interacting with Web services.
How accurately can one's interests be inferred from friends
Authors: Zhen Wen, Ching-Yung Lin
Venue: WWW
Year: 2010

Search and recommendation systems must effectively model user interests in order to provide personalized results. The proliferation of social software makes social network an increasingly important source for user interest modeling, because of the social influence and correlation among friends. However, there are large variations in people's contribution of social content. Therefore, it is impractical to accurately model interests for all users. As a result, applications need to decide whether to utilize a user interest model based on its accuracy. To address this challenge, we present a study on the accuracy of user interests inferred from three types of social content: social bookmarking, file sharing, and electronic communication, in an organizational social network within a large-scale enterprise. First, we demonstrate that combining different types of social content to infer user interests outperforms methods that use only one type of social content. Second, we present a technique to predict the inference accuracy based on easily observed network characteristics, including user activeness, network in-degree, out-degree, and betweenness centrality.
Learning to evaluate the visual quality of web pages
Authors: Ou Wu 0001, Yunfei Chen 0002, Bing Li 0001, Weiming Hu 0004
Venue: WWW
Year: 2010

A beautiful and well-laid out Web page greatly facilitates users' accessing and enhances browsing experiences. We use "visual quality (VQ)" to denote the aesthetics of Web pages. In this paper, a computational aesthetics approach is proposed to learn the evaluation model for the visual quality of Web pages. First, a Web page layout extraction algorithm (V-LBE) is introduced to partition a Web page into major layout blocks. Then, regarding a Web page as a semi-structured image, features known to significantly affect the visual quality of a Web page are extracted to construct a feature vector. The experimental results show the initial success of our approach. Potential applications include Web search and Web design.
Falconer: once SIOC meets semantic search engine
Authors: Gang Wu 0007, Mengdong Yang, Ke Wu, Guilin Qi, Yuzhong Qu
Venue: WWW
Year: 2010

Falconer is a semantic Web search engine enhanced SIOC (Semantically-Interlinked Online Communities) application, which is designed to demonstrate the ability of accelerating the creation and reuse process of semantic Web data with easy-to-use user interfaces. In this process, semantic Web search engines feed existing semantic data into the SIOC framework, where new semantic data are composed by the community and indexed again by those search engines. Compared to existing social (semantic) Web applications, Falconer inherently conforms to SIOC specification. It provides semantic search engine based user registration suggestion, friends auto-discovery, and semantic annotation for forum post content. Another distinctive feature is that it enables users to subscribe any resource having a URI as the topic they are interested in. The relationships among users, topics, and posts are further visualized for analyzing the topic trends in the community. As all semantic data are formatted in RDF and RDFa, they can be queried with SPARQL query language.
Are search engine users equally reliable?
Authors: Qianli Xing 0001, Yiqun Liu 0001, Rongwei Cen, Min Zhang 0006, Shaoping Ma, Liyun Ru
Venue: WWW
Year: 2010

In this paper, we study on the reliability of search engine users using click-through data. We proposed a graph-based approach to evaluate user reliability according to how users click on search result lists. We tried to incorporate this measure of reliability into relevance feedback for improving ranking performances. Experimental results indicate that the proposed approach is both effective and applicable.
RerankEverything: a reranking interface for browsing search results
Authors: Takehiro Yamamoto, Satoshi Nakamura 0002, Katsumi Tanaka
Venue: WWW
Year: 2010

This paper proposes a system called RerankEverything, which enables users to rerank search results in any search service, such as a Web search engine, an e-commerce site, a hotel reservation site and so on. In conventional search services, interactions between users and services are quite limited and complicated. In addition, search functions and interactions to refine search results differ depending on the services. By using RerankEverything, users can interactively explore search results in accordance with their interests by reranking search results from various viewpoints.
eduKEN: a tool for fine-grained video comment collection and analysis
Authors: Wai Gen Yee, Andrew Yates, Ophir Frieder, Armin Moehrle
Venue: WWW
Year: 2010

An increasing amount of Web information is in video format. Today's search technology allows videos to be found using graphical features and textual descriptions. However, the information gleaned from video features is coarse, while textual descriptions are often short and fail to capture the precise content of videos. We hypothesize that user comments contain supplemental information that effectively describes the content of a video. This information, once extracted, can be applied to a search engine index to improve video search accuracy.
LINKREC: a unified framework for link recommendation with user attributes and graph structure
Authors: Zhijun Yin, Manish Gupta 0001, Tim Weninger, Jiawei Han 0001
Venue: WWW
Year: 2010

With the phenomenal success of networking sites (e.g., Facebook, Twitter and LinkedIn), social networks have drawn substantial attention. On online social networking sites, link recommendation is a critical task that not only helps improve user experience but also plays an essential role in network growth. In this paper we propose several link recommendation criteria, based on both user attributes and graph structure. To discover the candidates that satisfy these criteria, link relevance is estimated using a random walk algorithm on an augmented social graph with both attribute and structure information. The global and local influence of the attributes is leveraged in the framework as well. Besides link recommendation, our framework can also rank attributes in a social network. Experiments on DBLP and IMDB data sets demonstrate that our method outperforms state-of-the-art methods based on network structure and node attribute information for link recommendation.
Automatic extraction of clickable structured web contents for name entity queries
Authors: Xiaoxin Yin, Wenzhao Tan, Xiao Li, Yi-Chin Tu
Venue: WWW
Year: 2010

Today the major web search engines answer queries by showing ten result snippets, which need to be inspected by users for identifying relevant results. In this paper we investigate how to extract structured information from the web, in order to directly answer queries by showing the contents being searched for. We treat users' search trails (i.e., post-search browsing behaviors) as implicit labels on the relevance between web contents and user queries. Based on such labels we use information extraction approach to build wrappers and extract structured information. An important observation is that many web sites contain pages for name entities of certain categories (e.g., AOL Music contains a page for each musician), and these pages have the same format. This makes it possible to build wrappers from a small amount of implicit labels, and use them to extract structured information from many web pages for different name entities. We propose STRUCLICK, a fully automated system for extracting structured information for queries containing name entities of certain categories. It can identify important web sites from web search logs, build wrappers from users' search trails, filter out bad wrappers built from random user clicks, and combine structured information from different web sites for each query. Comparing with existing approaches on information extraction, STRUCLICK can assign semantics to extracted data without any human labeling or supervision. We perform comprehensive experiments, which show STRUCLICK achieves high accuracy and good scalability.
A link-based similarity measure for scientific literature
Authors: Seok-Ho Yoon, Sang-Wook Kim, Sunju Park
Venue: WWW
Year: 2010

In this paper, we propose a new approach to measure sim-ilarities among academic papers based on their references. Our similarity measure uses both in-link and out-link by transforming in-link and out-link into undirected links.
'Follow me': a web-based, location-sharing architecture for large, indoor environments
Authors: Polychronis Ypodimatopoulos, Andrew Lippman
Venue: WWW
Year: 2010

We leverage the ubiquity of bluetooth-enabled devices and propose a decentralized, web-based architecture that allows users to share their location by following each other in the style of Twitter. We demonstrate a prototype that operates in a large building which generates a dataset of detected bluetooth devices at a rate of ~30 new devices per day, including the respective location where they were last detected. Users then query the dataset using their unique bluetooth ID and share their current location with their followers by means of unique URIs that they control. Our separation between producers (the building) and consumers (the users) of bluetooth device location data allows us to create socially-aware applications that respect user's privacy while limiting the software necessary to run on mobile devices to just a web browser.
Social group suggestion from user image collections
Authors: Jie Yu 0001, Xin Jin 0001, Jiawei Han 0001, Jiebo Luo 0001
Venue: WWW
Year: 2010

Photo-sharing services have attracted millions of people and helped construct massive social networks on the Web. A popular trend is that users share their image collections within social groups, which greatly promotes the interactions between users and expands their social networks. Existing systems have difficulties in generating satisfactory social group suggestions because the images are classified independently and their relationship in a collection is ignored. In this work, we intend to produce suggestions of suitable photo-sharing groups from a user's personal photo collection by mining images on the Web and leveraging the collection context. Both visual content and textual annotations are integrated to generate initial prediction of the events or topics depicted in the images. A user collection-based label propagation method is proposed to improve the group suggestion by modeling the relationship of images in the same collection as a sparse weighted graph. Experiments on real user images and comparisons with the state-of-the-art techniques demonstrate the effectiveness of the proposed approaches.
Review recommendation with graphical model and EM algorithm
Authors: Richong Zhang, Thomas T. Tran
Venue: WWW
Year: 2010

Automatically assessing the quality and helpfulness of consumer reviews is more and more desirable with the evolutionary development of online review systems. Existing helpfulness assessment methodologies make use of the positive vote fraction as a benchmark and heuristically find a "best guess" to estimate the helpfulness of review documents. This benchmarking methodology ignores the voter population size and treats the the same positive vote fraction as the same helpfulness value. We propose a review recommendation approach that make use of the probability density of the review helpfulness as the benchmark and exploit graphical model and Expectation Maximization (EM) algorithm for the inference of review helpfulness. The experimental results demonstrate that the proposed approach is superior to existing approaches.
ZoomRDF: semantic fisheye zooming on RDF data
Authors: Kang Zhang, Haofen Wang, Duc Thanh Tran, Yong Yu 0001
Venue: WWW
Year: 2010

With the development of Semantic Web in recent years, an increasing amount of semantic data has been created in form of Resource Description Framework (RDF). Current visualization techniques help users quickly understand the underlying RDF data by displaying its structure in an overview. However, detailed information can only be accessed by further navigation. An alternative approach is to display the global context as well as the local details simultaneously in a unified view. This view supports the visualization and navigation on RDF data in an integrated way. In this demonstration, we present ZoomRDF, a framework that: i) adapts a space-optimized visualization algorithm for RDF, which allows more resources to be displayed, thus maximizes the utilization of display space, ii) combines the visualization with a fisheye zooming concept, which assigns more space to some individual nodes while still preserving the overview structure of the data, iii) considers both the importance of resources and the user interaction on them, which offers more display space to those elements the user may be interested in. We implement the framework based on the Gene Ontology and demonstrate that it facilitates tasks like RDF data exploration and editing.
Optimizing user interaction for web-based mobile tasks
Authors: Dong Zhou, Ajay Chander, Hiroshi Inamura
Venue: WWW
Year: 2010

This paper describes the design and prototype implementation of MIntOS (Mobile Interaction Optimization System), a system for improving mobile interaction in web-based activities. MIntOS monitors users' interactions both for gathering interaction history and for the runtime construction of interaction context. A simple approach based on interaction burstiness is used to break interaction sequences into Trails, which approximates user tasks. Such Trails are used to generate rules for online, context-sensitive prediction of future interaction sequences. Predicted user interaction sequences are then optimized to reduce the amount of user input and user wait time using techniques such as interaction short-cuts, automatic text copying and form-filling, as well as page pre-fetching. Such optimized interaction sequences are, at real-time, recommended to the user through UI enhancements in a non-intrusive manner.