OpenRaiser/Intern-Atlas huggingface dataset:
SNDocRank: document ranking based on social networks
Authors: Liang Gou, Hung-Hsuan Chen, Jung-Hyun Kim, Xiaolong Zhang 0001, C. Lee Giles
Venue: WWW
Year: 2010

To improve the search results for socially-connect users, we propose a ranking framework, Social Network Document Rank (SNDocRank). This framework considers both document contents and the similarity between a searcher and document owners in a social network and uses a Multi-level Actor Similarity (MAS) algorithm to efficiently calculate user similarity in a social network. Our experiment results based on YouTube data show that compared with the tf-idf algorithm, the SNDocRank method returns more relevant documents of interest. Our findings suggest that in this framework, a searcher can improve search by joining larger social networks, having more friends, and connecting larger local communities in a social network.
Exploiting content redundancy for web information extraction
Authors: Pankaj Gulhane, Rajeev Rastogi, Srinivasan H. Sengamedu, Ashwin Tengli
Venue: WWW
Year: 2010

We propose a novel extraction approach that exploits content redundancy on the web to extract structured data from template-based web sites. We start by populating a seed database with records extracted from a few initial sites. We then identify values within the pages of each new site that match attribute values contained in the seed set of records. To filter out noisy attribute value matches, we exploit the fact that attribute values occur at fixed positions within template-based sites. We develop an efficient Apriori-style algorithm to systematically enumerate attribute position configurations with sufficient matching values across pages. Finally, we conduct an extensive experimental study with real-life web data to demonstrate the effectiveness of our extraction approach.
Exploring searcher interactions for distinguishing types of commercial intent
Authors: Qi Guo 0002, Eugene Agichtein
Venue: WWW
Year: 2010

An improved understanding of the relationship between search intent, result quality, and searcher behavior is crucial for improving the effectiveness of web search. While recent progress in user behavior mining has been largely focused on aggregate server-side click logs, we present a new search behavior model that incorporates fine-grained user interactions with the search results. We show that mining these interactions, such as mouse movements and scrolling, can enable more effective detection of the user's search intent. Potential applications include automatic search evaluation, improving search ranking, result presentation, and search advertising. As a case study, we report results on distinguishing between "research" and "purchase" variants of commercial intent, that show our method to be more effective than the current state-of-the-art.
Deep mashup: a description-based framework for lightweight integration of web contents
Authors: Hao Han, Junxia Guo, Takehiro Tokuda
Venue: WWW
Year: 2010

In this paper, we present a description-based mashup framework for lightweight integration of Web contents. Our implementation shows that we can integrate not only the Web services but also the Web applications easily, even the Web contents dynamically generated by client-side scripts.
How to consume linked data on the web: tutorial description
Authors: Olaf Hartig, Juan F. Sequeda, Jamie Taylor, Patrick Sinclair
Venue: WWW
Year: 2010

In the past two years, the amount of data published in RDF and following the Linked Data principles has increased dramatically. Everyday people are publishing datasets as Linked Data. However, applications that consume Linked Data are not mainstream yet. To overcome this issue, we present a beginners tutorial on consuming Linked Data. We will discuss existing techniques how users can currently consume Linked Data and use it in their current applications.
Estimation of user interest in visited web page
Authors: Michal Holub, Mária Bieliková
Venue: WWW
Year: 2010

Nowadays web portals contain large amount of information that is meant for various visitors or groups of visitors. To effectively navigate within the content the website needs to "know" its users in order to provide personalized content to them. We propose a method for automatic estimation of the user's interest in a web page he visits. This estimation is used for the recommendation of web portal pages (through presenting adaptive links) that the user might like. We conducted series of experiments in the domain of our faculty web portal to evaluate proposed approach.
Multi-modality in one-class classification
Authors: Matthijs Hovelynck, Boris Chidlovskii
Venue: WWW
Year: 2010

We propose a method for improving classification performance in a one-class setting by combining classifiers of different modalities. We apply the method to the problem of distinguishing responsive documents in a corpus of e-mails, like Enron Corpus. We extract the social network of actors which is implicit in a large body of electronic communication and turn it into valuable features for classifying the exchanged documents. Working in a one-class setting we folow a semi-supervised approach based on the Mapping Convergence framework. We propose an alternative interpretation, that allows for broader applicability when positive and negative items are not naturally separable. We propose an extension to the one-class evaluation framework in truly one-case cases when only some positive training examples are available. We extent the one-class setting to the co-training principle that enables us to take advantage of multiple views on the data. We report evaluation results of this extension on three different corpora including Enron Corpus.
Exploring web scale language models for search query processing
Authors: Jian Huang 0002, Jianfeng Gao 0001, Jiangbo Miao, Xiaolong Li, Kuansan Wang, Fritz Behr, C. Lee Giles
Venue: WWW
Year: 2010

It has been widely observed that search queries are composed in a very different style from that of the body or the title of a document. Many techniques explicitly accounting for this language style discrepancy have shown promising results for information retrieval, yet a large scale analysis on the extent of the language differences has been lacking. In this paper, we present an extensive study on this issue by examining the language model properties of search queries and the three text streams associated with each web document: the body, the title, and the anchor text. Our information theoretical analysis shows that queries seem to be composed in a way most similar to how authors summarize documents in anchor texts or titles, offering a quantitative explanation to the observations in past work.
Selectivity estimation for SPARQL graph pattern
Authors: Hai Huang 0003, Chengfei Liu
Venue: WWW
Year: 2010

This paper focuses on selectivity estimation for SPARQL graph patterns, which is crucial to RDF query optimization. The previous work takes the join uniformity assumption, which would lead to high inaccurate estimation in the cases where properties in SPARQL graph patterns are correlated. We take into account the dependencies among properties in SPARQL graph patterns and propose a more accurate estimation model. We first focus on two common SPARQL graph patterns (star and chain patterns) and propose to use Bayesian network and chain histogram for estimating the selectivityof them. Then, for an arbitrary composite SPARQL graph pattern, we maximally combines the results of the star and chain patterns we have precomputed. The experiments show that our method outperforms existing approaches in accuracy.
Exposing audio data to the web: an API and prototype
Authors: David Humphrey, Corban Brook, Alistair MacDonald
Venue: WWW
Year: 2010

The HTML5 specification introduces the audio and video media elements, and with them the opportunity to change the way media is integrated on the web. The current HTML5 media API provides ways to play and get limited information about audio and video, but no way to programatically access or create such media. In this paper we present an enhanced API for these media elements, as well as details about a Mozilla Firefox implementation created by the authors, which allows web developers to read and write raw audio data.
Optimal marketing and pricing over social networks
Authors: Nicole Immorlica, Vahab S. Mirrokni
Venue: WWW
Year: 2010

We discuss the use of social networks in implementing viral marketing strategies. In the first part of this tutorial, we study influence maximization or how the structure of the social network affects the spread of behaviors and technologies. In the second part, we then consider how one might monopolize these natural processes to generate revenue in a revenue maximization setting.
Enabling entity-based aggregators for web 2.0 data
Authors: Ekaterini Ioannou, Claudia Niederée, Yannis Velegrakis
Venue: WWW
Year: 2010

Selecting and presenting content culled from multiple heterogeneous and physically distributed sources is a challenging task. The exponential growth of the web data in modern times has brought new requirements to such integration systems. Data is not any more produced by content providers alone, but also from regular users through the highly popular Web 2.0 social and semantic web applications. The plethora of the available web content increased its demand by regular users who could not any more wait the development of advanced integration tools. They wanted to be able to build in a short time their own specialized integration applications. Aggregators came to the risk of these users. They allowed them not only to combine distributed content, but also to process it in ways that generate new services available for further consumption.
A scalable machine-learning approach for semi-structured named entity recognition
Authors: Utku Irmak, Reiner Kraft 0001
Venue: WWW
Year: 2010

Named entity recognition studies the problem of locating and classifying parts of free text into a set of predefined categories. Although extensive research has focused on the detection of person, location and organization entities, there are many other entities of interest, including phone numbers, dates, times and currencies (to name a few examples). We refer to these types of entities as "semi-structured named entities", since they usually follow certain syntactic formats according to some conventions, although their structure is typically not well-defined. Regular expression solutions require significant amount of manual effort and supervised machine learning approaches rely on large sets of labeled training data. Therefore, these approaches do not scale when we need to support many semi-structured entity types in many languages and regions.
Antourage: mining distance-constrained trips from flickr
Authors: Saral Jain, Stephan Seufert, Srikanta J. Bedathur
Venue: WWW
Year: 2010

We study how to automatically extract tourist trips from large volumes of geo-tagged photographs. Working with more than 8 million of these photographs that are publicly available via photo- sharing communities such as Flickr and Panoramio, our goal is to satisfy the needs of a tourist who specifies a starting location (typically a hotel) together with a bounded travel distance and demands a tour that visits the popular sites along the way. Our system, named ANTOURAGE, solves this intractable problem using a novel adaptation of the max-min ant system (MMAS) meta-heuristic. Experiments using GPS metadata crawled from Flickr show that ANTOURAGE can generate high-quality tours.
Analyzing collective view of future, time-referenced events on the web
Authors: Adam Jatowt, Hideki Kawai, Kensuke Kanazawa, Katsumi Tanaka, Kazuo Kunieda, Keiji Yamada
Venue: WWW
Year: 2010

Humans have always desired to guess the future in order to adapt their behavior and maximize chances of success. In this paper, we conduct exploratory analysis of future-related information on the web. We focus on the future-related information which is grounded in time, that is, the information on forthcoming events whose expected occurrence dates are already known. We collect data by crawling search engine index and analyze collective view of future time-referenced events discussed on the web.
Optimizing two stage bigram language models for IR
Authors: Sara Javanmardi, Jianfeng Gao 0001, Kuansan Wang
Venue: WWW
Year: 2010

Although higher order language models (LMs) have shown benefit of capturing word dependencies for Information retrieval(IR), the tuning of the increased number of free parameters remains a formidable engineering challenge. Consequently,in many real world retrieval systems, applying higher order LMs is an exception rather than the rule. In this study, we address the parameter tuning problem using a framework based on a linear ranking model in which different component models are incorporated as features. Using unigram and bigram LMs with 2 stage smoothing as examples, we show that our method leads to a bigram LM that outperforms significantly its unigram counterpart and the well-tuned BM25 model.
Web search/browse log mining: challenges, methods, and applications
Authors: Daxin Jiang, Jian Pei 0001, Hang Li 0001
Venue: WWW
Year: 2010

Huge amounts of search and browse log data has been accumulated in various search engines. Such massive search/browse log data, on the one hand, provides great opportunities to mine the wisdom of crowds and improve Web search as well as online advertisement. On the other hand, designing effective and efficient algorithms and tools to clean, model, and process large scale log data presents great challenges.
GRAPE: a system for disambiguating and tagging people names in web search
Authors: Lili Jiang 0002, Wei Shen 0004, Jianyong Wang 0001, Ning An 0001
Venue: WWW
Year: 2010

Name ambiguity is a big challenge in people information retrieval and has received considerable attention, especially with the increasing volume of Web data in recent years. In this demo, we present a system, GRAPE, which is capable of finding people related information over the Web. The salient features of our system are people name disambiguation and people tag presentation, which effectively distinguish different people entities sharing the same name and uniquely represent each namesake with a cluster of tags, such as occupation, birthdate, and organization.
iRIN: image retrieval in image-rich information networks
Authors: Xin Jin 0001, Jiebo Luo 0001, Jie Yu 0001, Gang Wang 0012, Dhiraj Joshi, Jiawei Han 0001
Venue: WWW
Year: 2010

In this demo, we present a system called iRIN designed for performing image retrieval in image-rich information networks. We first introduce MoK-SimRank to significantly improve the speed of SimRank, one of the most popular algorithms for computing node similarity in information networks. Next, we propose an algorithm called SimLearn to (1) extend MoK-SimRank to heterogeneous image-rich information network, and (2) account for both link-based and content-based similarities by seamlessly integrating reinforcement learning with feature learning.
Topic initiator detection on the world wide web
Authors: Xin Jin 0001, W. Scott Spangler, Rui Ma, Jiawei Han 0001
Venue: WWW
Year: 2010

In this paper we introduce a new Web mining and search technique - Topic Initiator Detection (TID) on the Web. Given a topic query on the Internet and the resulting collection of time-stamped web documents which contain the query keywords, the task of TID is to automatically return which web document (or its author) initiated the topic or was the first to discuss about the topic.
Applications of open search tools
Authors: Rosie Jones, Ted Drake
Venue: WWW
Year: 2010

It costs about 300M dollars to just build a search system that scales to the web. Web services which open up web search results to the public allow academics, developers, and entrepreneurs to achieve instant web search parity for free and enable them to focus on building their additional secret sauce on top to create even grander relevant services. For example, a social network could leverage open search and their data about users to personalize web search. Additionally, one of the best ways to gather data about web search behavior is to build your own search system. Proto-type IR and web search systems based on open search can be used to gather user interaction data and test the applicability of research ideas. Open Source tools like Lucene and Nutch and open search services like from major search engines can greatly help developers implement these types of systems quickly, allowing for fast production and evaluation. We will give detailed overviews of the popular open search tools, showcasing examples of search and IR algorithms and systems implemented using them, as well as discussing how evaluation can be performed.
Live web search experiments for the rest of us
Authors: Timothy Jones 0001, David Hawking, Ramesh S. Sankaranarayana
Venue: WWW
Year: 2010

There are significant barriers to academic research into user Web search preferences. Academic researchers are unable to manipulate the results shown by a major search engine to users and would have no access to the interaction data collected by the engine. Our initial approach to overcoming this was to ask participants to submit queries to an experimental search engine rather than their usual search tool. Over several different experiments we found that initial user buy-in was high but that people quickly drifted back to their old habits and stopped contributing data. Here, we report our investigation of possible reasons why this occurs. An alternative approach is exemplified by the Lemur browser toolbar, which allows local collection of user interaction data from search engine sessions, but does not allow result pages to be modified. We will demonstrate a new Firefox toolbar that we have developed to support experiments in which search results may be arbitrarily manipulated. Using our toolbar, academics can set up the experiments they want to conduct, while collecting (subject to human experimentation guidelines) queries, clicks and dwell times as well as optional explicit judgments.
The utility of tweeted URLs for web search
Authors: Vasileios Kandylas, Ali Dasdan
Venue: WWW
Year: 2010

Microblogging as introduced by Twitter is becoming a source of tracking real-time news. Although identifying the highest quality or most useful posts or tweets from Twitter for breaking news is still an open problem, major web search engines seem convinced of the value of such posts and have already started allocating part of their search results pages to them. In this paper, we study a different aspect of the problem for a search engine: instead of the value of the posts, we study the value of the (shortened) URLs referenced in these posts. Our results indicate that unlike frequently bookmarked URLs, which are generally of high quality, frequently tweeted URLs tend to fall in two opposite categories: they are either high in quality, or they are spam. Identifying the quality category of a URL is not trivial, but the combination of characteristics can reveal some trends.
Large-scale bot detection for search engines
Authors: Hongwen Kang, Kuansan Wang, David Soukal, Fritz Behr, Zijian Zheng 0002
Venue: WWW
Year: 2010

In this paper, we propose a semi-supervised learning approach for classifying program (bot) generated web search traffic from that of genuine human users. The work is motivated by the challenge that the enormous amount of search data pose to traditional approaches that rely on fully annotated training samples. We propose a semi-supervised framework that addresses the problem in multiple fronts. First, we use the CAPTCHA technique and simple heuristics to extract from the data logs a large set of training samples with initial labels, though directly using these training data is problematic because the data thus sampled are biased. To tackle this problem, we further develop a semi-supervised learning algorithm to take advantage of the unlabeled data to improve the classification performance. These two proposed algorithms can be seamlessly combined and very cost efficient to scale the training process. In our experiment, the proposed approach showed significant (i.e. 2:1) improvement compared to the traditional supervised approach.
Trend detection model
Authors: Noriaki Kawamae, Ryuichiro Higashinaka
Venue: WWW
Year: 2010

This paper presents a topic model that detects topic distributions over time. Our proposed model, Trend Detection Model (TDM) introduces a latent trend class variable into each document. The trend class has a probability distribution over topics and a continuous distribution over time. Experiments using our data set show that TDM is useful as a generative model in the analysis of the evolution of trends.
Unsupervised query segmentation using click data: preliminary results
Authors: Julia Kiseleva, Qi Guo 0002, Eugene Agichtein, Daniel Billsus, Wei Chai
Venue: WWW
Year: 2010

We describe preliminary results of experiments with an unsupervised framework for query segmentation, transforming keyword queries into structured queries. The resulting queries can be used to more accurately search product databases, and potentially improve result presentation and query suggestion. The key to developing an accurate and scalable system for this task is to train a query segmentation or attribute detection system over labeled data, which can be acquired automatically from query and click-through logs. The main contribution of our work is a new method to automatically acquire such training data - resulting in significantly higher segmentation performance, compared to previously reported methods.
LCA-based selection for XML document collections
Authors: Georgia Koloniari, Evaggelia Pitoura
Venue: WWW
Year: 2010

In this paper, we address the problem of database selection for XML document collections, that is, given a set of collections and a user query, how to rank the collections based on their goodness to the query. Goodness is determined by the relevance of the documents in the collection to the query.
Detecting epidemic tendency by mining search logs
Authors: Weize Kong, Yiqun Liu 0001, Shaoping Ma, Liyun Ru
Venue: WWW
Year: 2010

We consider the problem of detecting epidemic tendency by mining search logs. We propose an algorithm based on click-through information to select epidemic related queries/terms. We adopt linear regression to model epidemic occurrences and frequencies of epidemic related terms (ERTs) in search logs. The results show our algorithm is effective in finding ERTs which obtain a high correlation value with epidemic occurrences. We also find the proposed method performs better when combining different ERTs than using single ERT.
Stop thinking, start tagging: tag semantics emerge from collaborative verbosity
Authors: Christian Körner, Dominik Benz, Andreas Hotho, Markus Strohmaier, Gerd Stumme
Venue: WWW
Year: 2010

Windows and Mac users to share the same directory structure and access a common set of files.