OpenRaiser/Intern-Atlas huggingface dataset:
Personalized search on Flickr based on searcher's preference prediction
Authors: Dongyuan Lu, Qiudan Li
Venue: WWW
Year: 2011

In this paper, we propose a personalized search model to assist users in obtaining interested photos on Flickr, which exploits the favorite marks of the searcher's friends to predict the searcher's preference on the returned photos. The proposed model utilizes a co-clustering method to extract latent interest dimensions from users' implicit interests, and employs a discriminative learning method to predict searcher's preference on the returned photos. Preliminary experiments demonstrate the improvement of the proposed model compared to existing one-fit-all methods and a user-based collaborative filtering method.
A classification based framework for concept summarization
Authors: Dhruv Kumar Mahajan, Sundararajan Sellamanickam, Subhajit Sanyal, Amit Madaan
Venue: WWW
Year: 2011

In this paper we propose a novel classification based framework for finding a small number of images that summarize a given concept. Our method exploits metadata information available with the images to get category information using Latent Dirichlet Allocation. Using this category information for each image, we solve the underlying classification problem by building a sparse classifier model for each concept. We demonstrate that the images that specify the sparse model form a good summary. In particular, our summary satisfies important properties such as likelihood, diversity and balance in both visual and semantic sense. Furthermore, the framework allows users to specify desired distributions over categories to create personalized summaries. Experimental results on seven broad query types show that the proposed method performs better than state-of-the-art methods.
Measurement and analysis of cyberlocker services
Authors: Aniket Mahanti
Venue: WWW
Year: 2011

Cyberlocker Services (CLS) such as RapidShare and Megaupload have recently become popular. The decline of Peerto-Peer (P2P) file sharing has prompted various services including CLS to replace it. We propose a comprehensive multi-level characterization of the CLS ecosystem. We answer three research questions: (a) what is a suitable measurement infrastructure for gathering CLS workloads; (b) what are the characteristics of the CLS ecosystem; and (c) what are the implications of CLS on Web 2.0 (and the Internet). To the best of our knowledge, this work is the first to characterize the CLS ecosystem. The work will highlight the content, usage, performance, infrastructure, quality of service, and evolution characteristics of CLS.
Fuzzy associative rule-based approach for pattern mining and identification and pattern-based classification
Authors: Ashish Mangalampalli, Vikram Pudi
Venue: WWW
Year: 2011

Associative Classification leverages Association Rule Mining (ARM) to train Rule-based classifiers. The classifiers are built on high quality Association Rules mined from the given dataset. Associative Classifiers are very accurate because Association Rules encapsulate all the dominant and statistically significant relationships between items in the dataset. They are also very robust as noise in the form of insignificant and low-frequency itemsets are eliminated during the mining and training stages. Moreover, the rules are easy-to-comprehend, thus making the classifier transparent.
A feature-pair-based associative classification approach to look-alike modeling for conversion-oriented user-targeting in tail campaigns
Authors: Ashish Mangalampalli, Adwait Ratnaparkhi, Andrew O. Hatch, Abraham Bagherjeiran, Rajesh Parekh, Vikram Pudi
Venue: WWW
Year: 2011

Online advertising offers significantly finer granularity, which has been leveraged in state-of-the-art targeting methods, like Behavioral Targeting (BT). Such methods have been further complemented by recent work in Look-alike Modeling (LAM) which helps in creating models which are customized according to each advertiser's requirements and each campaign's characteristics, and which show ads to users who are most likely to convert on them, not just click them. In Look-alike Modeling given data about converters and nonconverters, obtained from advertisers, we would like to train models automatically for each ad campaign. Such custom models would help target more users who are similar to the set of converters the advertiser provides. The advertisers get more freedom to define their preferred sets of users which should be used as a basis to build custom targeting models. In behavioral data, the number of conversions (positive class) per campaign is very small (conversions per impression for the advertisers in our data set are much less than 10-4), giving rise to a highly skewed training dataset, which has most records pertaining to the negative class. Campaigns with very few conversions are called as tail campaigns, and those with many conversions are called head campaigns. Creation of Look-alike Models for tail campaigns is very challenging and tricky using popular classifiers like Linear SVM and GBDT, because of the very few number of positive class examples such campaigns contain. In this paper, we present an Associative Classification (AC) approach to LAM for tail campaigns. Pairs of features are used to derive rules to build a Rule-based Associative Classifier, with the rules being sorted by frequency-weighted log-likelihood ratio (F-LLR). The top k rules, sorted by F-LLR, are then applied to any test record to score it. Individual features can also form rules by themselves, though the number of such rules in the top k rules and the whole rule-set is very small. Our algorithm is based on Hadoop, and is thus very efficient in terms of speed.
The design and usage of tentative events for time-based social coordination in the enterprise
Authors: Mikhil Masli, Werner Geyer, Casey Dugan, Beth Brownholtz
Venue: WWW
Year: 2011

Existing enterprise calendaring systems have suffered from problems like rigidity, lack of transparency, and poor integration with social networks. We present the system design and rationale for a novel social coordination mechanism, called "Suggestions," that addresses these issues. Our system integrates ideas drawn from designs of lightweight polling systems and one's social network into an open calendar tool, providing a space for users to coordinate, socialize around, or negotiate the "what" and the "when" of their events. Suggestions was released inside a large enterprise setting, where initial interviews revealed users' thoughts on transparent scheduling, reaching wider audiences and task appropriateness, and suggested ways to improve our design.
Mobile topigraphy: large-scale tag cloud visualization for mobiles
Authors: Tatsushi Matsubayashi, Katsuhiko Ishiguro
Venue: WWW
Year: 2011

We introduce a new mobile topigraphy system that uses the contour map metaphor to display large-scale tag clouds. We introduce the technical issues for topigraphy, and recent requirements for and developments in mobile interfaces. We also present some applications for our mobile topigraphy system and describe the assessment on two initial applications.
We know who you followed last summer: inferring social link creation times in twitter
Authors: Brendan Meeder, Brian Karrer, Amin Sayedi, R. Ravi, Christian Borgs, Jennifer T. Chayes
Venue: WWW
Year: 2011

Understanding a network's temporal evolution appears to require multiple observations of the graph over time. These often expensive repeated crawls are only able to answer questions about what happened from observation to observation, and not what happened before or between network snapshots. Contrary to this picture, we propose a method for Twitter's social network that takes a single static snapshot of network edges and user account creation times to accurately infer when these edges were formed. This method can be exact in theory, and we demonstrate empirically for a large subset of Twitter relationships that it is accurate to within a few hours in practice.
Performance enhancement of scheduling algorithms in clusters and grids using improved dynamic load balancing techniques
Authors: Hemant Kumar Mehta, Priyesh Kanungo, Manohar Chandwani
Venue: WWW
Year: 2011

This paper describes the research work done for during PhD study. Cluster computing, grid computing and cloud computing are distributed computing environments (DCEs) widely accepted for the next generation Web based commercial and scientific applications. These applications work around the globally distributed data of petabyte scale that can only be processed by the aggregating the capability of globally distributed resources. The resource management and process scheduling in large scale distributed computing environment are a challenging task. In this research work we have devised new scheduling algorithms and resource management strategies specially designed for the cluster and grid cloud and peer-to-peer computing. The research work finally presented the distributed computing solutions to one scientific and one commercial application viz. e-Learning and data mining.
Citizen sensor data mining, social media analytics and development centric web applications
Authors: Meenakshi Nagarajan, Amit P. Sheth, Selvam Velmurugan
Venue: WWW
Year: 2011

With the rapid rise in the popularity of social media (500M+ Facebook users, 100M+ twitter users), and near ubiquitous mobile access (4+ billion actively-used mobile phones), the sharing of observations and opinions has become common-place (nearly 100M tweets a day, 1.8 trillion SMSs in US last year). This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it towards targeted online content delivery, crisis management, organizing revolutions or promoting social development in underdeveloped and developing countries.
A self organizing document map algorithm for large scale hyperlinked data inspired by neuronal migration
Authors: Kotaro Nakayama, Yutaka Matsuo
Venue: WWW
Year: 2011

Web document clustering is one of the research topics that is being pursued continuously due to the large variety of applications. Since Web documents usually have variety and diversity in terms of domains, content and quality, one of the technical difficulties is to find a reasonable number and size of clusters. In this research, we pay attention to SOMs (Self Organizing Maps) because of their capability of visualized clustering that helps users to investigate characteristics of data in detail. The SOM is widely known as a "scalable" algorithm because of its capability to handle large numbers of records. However, it is effective only when the vectors are small and dense. Although several research efforts on making the SOM scalable have been conducted, technical issues on scalability and performance for sparse high-dimensional data such as hyperlinked documents still remain. In this paper, we introduce MIGSOM, an SOM algorithm inspired by a recent discovery on neuronal migration. The two major advantages of MIGSOM are its scalability for sparse high-dimensional data and its clustering visualization functionality. In this paper, we describe the algorithm and implementation, and show the practicality of the algorithm by applying MIGSOM to a huge scale real data set: Wikipedia's hyperlink data.
Eighth workshop on information integration on the web (IIWeb 2011)
Authors: Ullas B. Nambiar, L. Venkata Subramaniam
Venue: WWW
Year: 2011

The goal of the 8th Workshop on Information Integration on the Web (IIWeb) is to bring together academic researchers and industry practitioners in Information Integration with a special focus on integrating cyber physical systems for building a sustainable ecosystem for life on our planet. Towards this goal the workshop program consists of an engaging set of talks and papers.
Dynamic learning-based mechanism design for dependent valued exchange economies
Authors: Swaprava Nath
Venue: WWW
Year: 2011

Learning private information from multiple strategic agents poses challenge in many Internet applications. Sponsored search auctions, crowdsourcing, Amazon's mechanical turk, various online review forums are examples where we are interested in learning true values of the advertisers or true opinion of the reviewers. The common thread in these decision problems is that the optimal outcome depends on the private information of all the agents, while the decision of the outcome can be chosen only through reported information which may be manipulated by the strategic agents. The other important trait of these applications is their dynamic nature. The advertisers in an online auction or the users of mechanical turk arrive and depart, and when present, interact with the system repeatedly, giving the opportunity to learn their types. Dynamic mechanisms, which learn from the past interactions and make present decisions depending on the expected future evolution of the game, has been shown to improve performance over repeated versions of static mechanisms. In this paper, we will survey the past and current state-of-the-art dynamic mechanisms and analyze a new setting where the agents consist of buyers and sellers, known as exchange economies, and agents having value interdependency, which are relevant in applications illustrated through examples. We show that known results of dynamic mechanisms with independent value settings cannot guarantee certain desirable properties in this new significantly different setting. In the future work, we propose to analyze similar settings with dynamic types and population.
Sentence-level contextual opinion retrieval
Authors: Sylvester Olubolu Orimaye
Venue: WWW
Year: 2011

Existing opinion retrieval techniques do not provide context-dependent relevant results. Most of the approaches used by state-of-the-art techniques are based on frequency of query terms, such that all documents containing query terms are retrieved, regardless of contextual relevance to the intent of the human seeking the opinion. However, in a particular opinionated document, words could occur in different contexts, yet meet the frequency attached to a certain opinion threshold, thus explicitly creating a bias in overall opinion retrieved. In this paper we propose a sentence-level contextual model for opinion retrieval using grammatical tree derivations and approval voting mechanism. Model evaluation performed between our contextual model, BM25, and language model shows that the model can be effective for contextual opinion retrieval such as faceted opinion retrieval.
Evaluating new search engine configurations with pre-existing judgments and clicks
Authors: Umut Ozertem, Rosie Jones, Benoît Dumoulin
Venue: WWW
Year: 2011

We provide a novel method of evaluating search results, which allows us to combine existing editorial judgments with the relevance estimates generated by click-based user browsing models. There are evaluation methods in the literature that use clicks and editorial judgments together, but our approach is novel in the sense that it allows us to predict the impact of unseen search models without online tests to collect clicks and without requesting new editorial data, since we are only re-using existing editorial data, and clicks observed for previous result set configurations. Since the user browsing model and the pre-existing editorial data cannot provide relevance estimates for all documents for the selected set of queries, one important challenge is to obtain this performance estimation where there are a lot of ranked documents with missing relevance values. We introduce a query and rank based smoothing to overcome this problem. We show that a hybrid of these smoothing techniques performs better than both query and position based smoothing, and despite the high percentage of missing judgments, the resulting method is significantly correlated (0.74) with DCG values evaluated using fully judged datasets, and approaches inter-annotator agreement. We show that previously published techniques, applicable to frequent queries, degrade when applied to a random sample of queries, with a correlation of only 0.29. While our experiments focus on evaluation using DCG, our method is also applicable to other commonly used metrics.
Assistive technology for vision-impairments: anagenda for the ICTD community
Authors: Joyojeet Pal, Manas Pradhan, Mihir Shah, Rakesh Babu
Venue: WWW
Year: 2011

In recent years, ICTD (Information Communications Technology and Development) has grown in significance as an area of engineering research that has focused on low-cost appropriate technologies for the needs of a developing world largely underserved by the dominant modes of technology design. Assistive Technologies (AT) used by people with disabilities facilitate greater equity in the social and economic public sphere. However, by and large such technologies are designed in the industrialized world, for people living in those countries. This is especially true in the case of AT for people with vision impairments -- market-prevalent technologies are both very expensive and are built to support the language and infrastructure typical in the industrialized world. While the community of researchers in the Web Accessibility space have made significant strides, the operational concerns of networks in the developing world, as well as challenges in support for new languages and contexts raises a new set of challenges for technologists in this space. We discuss the state of various technologies in the context of the developing world and propose directions in scientific and community-contributed efforts to increase the relevance and access to AT and accessibility in the developing world.
A tool for fast indexing and querying of graphs
Authors: Dipali Pal, Praveen R. Rao
Venue: WWW
Year: 2011

We present a tool called GiS for indexing and querying a large database of labeled, undirected graphs. Such graphs can model chemical compounds, represent contact maps constructed from 3D structure of proteins, and so forth. GiS supports exact subgraph matching and approximate graph matching queries. It adopts a suite of new techniques and algorithms for (a) fast construction of disk-based indexes with small index sizes, and (b) efficient query processing with high precision of matching. During the demo, the user can index real graph datasets using a recommendation facility in GiS, pose exact subgraph matching and approximate graph matching queries, and view matching graphs using the Jmol browser.
Collaborative classification over P2P networks
Authors: Odysseas Papapetrou, Wolf Siberski, Stefan Siersdorfer
Venue: WWW
Year: 2011

We propose a novel collaborative approach for distributed document classification, combining the knowledge of multiple users for improved organization of data such as individual document repositories or emails. The approach builds on top of a P2P network and outperforms the state of the art approaches in collaborative classification.
A user-tunable approach to marketplace search
Authors: Nish Parikh, Neel Sundaresan
Venue: WWW
Year: 2011

The notion of relevance is key to the performance of search engines as they interpret the user queries and respond with matching results. Online search engines have used other features beyond pure IR features to return relevant matching documents. However, over-emphasis on relevance could lead to redundancy in search results. In document search, diversity is simply the variety of documents that span the result set. In an online marketplace the diversity in the result set is represented by items for sale by different sellers at different prices with different sales options. For such a marketplace, in order to minimize query abandonment and the risk of dissatisfaction to the average user, several factors like diversity, trust and value need to be taken into account. Previous work in this field [4] has shown an impossibility result that there exists no such function that can optimize for all these factors. Since these factors and the measures associated with the factors could be subjective we take an approach of giving the control back to the user. In this paper we describe an interface which enables users to have more control over the optimization function used to present the results. We demonstrate this for search on eBay - one of the largest online marketplaces with a vibrant user community and dynamic inventory. We use an algorithm based on bounded greedy selection [5] to construct the result set based on parameters specified by the user.
The computer is the new sewing machine: benefits and perils of crowdsourcing
Authors: Praveen K. Paritosh, Panos Ipeirotis, Matt Cooper, Siddharth Suri
Venue: WWW
Year: 2011

There is increased participation by the developing world in the global manufacturing marketplace: the sewing machine in Bangladesh can be a means to support an entire family. Crowdsourcing for cognitive tasks consists of asking humans for questions that are otherwise impossible to answer by algorithms, e.g., is this image pornographic, are these two addresses the same, what is the translation for this text in French? In the last five years, there has been an exponential growth in the size of the global cognitive marketplace: Amazon.com's Mechanical Turk has an estimated 500,000 active workers in over 100 countries, and there are dozens of other companies in this space. This turns the computer into a modern-day sewing machine, where cognitive work of various levels of difficulty will pay anywhere from 5 to 50 dollars a day. Unlike outsourcing, which usually requires college education, competence at these tasks might be a month or even less of training. At its best, this could be a powerful bootstrap for a billion people. At its worst, this can lead to unprecedented exploitation. In this panel, we discuss the technical, social and economic questions and implications that a global cognitive marketplace raises.
Web-based open-domain information extraction
Authors: Marius Pasca
Venue: WWW
Year: 2011

This tutorial provides an overview of extraction methods developed in the area of Web-based open-domain information extraction, whose purpose is the acquisition of open-domain classes, instances and relations from Web text. The extraction methods operate over unstructured or semi-structured text. They take advantage of weak supervision provided in the form of seed examples or small amounts of annotated data, or draw upon knowledge already encoded within resources created strictly by experts or collaboratively by users. The tutorial teaches the audience about existing resources that include instances and relations; details of methods for extracting such data from structured and semi-structured text available on the Web; and strengths and limitations of resources extracted from text as part of recent literature, with applications in knowledge discovery and information retrieval.
Generalized fact-finding
Authors: Jeff Pasternack, Dan Roth
Venue: WWW
Year: 2011

Once information retrieval has located a document, and information extraction has provided its contents, how do we know whether we should actually believe it? Fact-finders are a state-of-the-art class of algorithms that operate in a manner analogous to Kleinberg's Hubs and Authorities, iteratively computing the trustworthiness of an information source as a function of the believability of the claims it makes, and the believability of a claim as a function of the trustworthiness of those sources asserting it. However, as fact-finders consider only "who claims what", they ignore a great deal of relevant background and contextual information. We present a framework for "lifting" (generalizing) the fact-finding process, allowing us to elegantly incorporate knowledge such as the confidence of the information extractor and the attributes of the information sources. Experiments demonstrate that leveraging this information significantly improves performance over existing, "unlifted" fact-finding algorithms.