OpenRaiser/Intern-Atlas huggingface dataset:
Learning to tokenize web domains
Authors: Sriram Srinivasan, Sourangshu Bhattacharya
Venue: WWW
Year: 2011

Domain Match is an Internet monetization product offered by web companies like Yahoo! The product offers display of ads and search results, when a user requests a webpage from a domain which is non-existent or does not have any content. This product earns significant amount of advertising revenue for major internet companies like Yahoo! Hence it is an important product receiving millions of queries per day. Domain Match (DM) works by tokenizing the input domains and sub-folders into keywords and then displaying ads and search results queried on the keywords. In this poster, we describe a machine learning based solution, which automatically learns to tokenize new domains, given a training dataset containing a set of domains and their tokenizations. We use positional frequency and parts of speech as features for scoring tokens. Tokens are scored combined using various scoring models. We compare two ways of training the models: a simple gain function based training and a large margin training. Experimental results are encouraging.
Coverage patterns for efficient banner advertisement placement
Authors: Bhargav Sripada, Krishna Reddy Polepalli, Rage Uday Kiran
Venue: WWW
Year: 2011

In an online banner advertising scenario, an advertiser expects that the banner advertisement should be displayed to certain percentage of web site visitors. In this context, to generate more revenue for a given web site, the publisher has to meet the demands of several advertisers by providing appropriate sets of web pages. To help the publishers and advertisers, in this paper, we propose a model of coverage patterns and a methodology to extract potential coverage patterns by analyzing click stream data. Given web pages of a site, a coverage pattern is a set of web pages visited by a certain percentage of visitors. The proposed approach has the potential to enable the publisher in meeting the demands of several advertisers. The efficiency and advantages of the proposed approach is shown by conducting experiments on real world data sets.
Learning to rank with multiple objective functions
Authors: Krysta M. Svore, Maksims Volkovs, Christopher J. C. Burges
Venue: WWW
Year: 2011

We investigate the problem of learning to rank with document retrieval from the perspective of learning for multiple objective functions. We present solutions to two open problems in learning to rank: first, we show how multiple measures can be combined into a single graded measure that can be learned. This solves the problem of learning from a 'scorecard' of measures by making such scorecards comparable, and we show results where a standard web relevance measure (NDCG) is used for the top-tier measure, and a relevance measure derived from click data is used for the second-tier measure; the second-tier measure is shown to significantly improve while leaving the top-tier measure largely unchanged. Second, we note that the learning-to-rank problem can itself be viewed as changing as the ranking model learns: for example, early in learning, adjusting the rank of all documents can be advantageous, but later during training, it becomes more desirable to concentrate on correcting the top few documents for each query. We show how an analysis of these problems leads to an improved, iteration-dependent cost function that interpolates between a cost function that is more appropriate for early learning, with one that is more appropriate for late-stage learning. The approach results in a significant improvement in accuracy with the same size models. We investigate these ideas using LambdaMART, a state-of-the-art ranking algorithm.
Using complex network features for fast clustering in the web
Authors: Jintao Tang, Ting Wang, Ji Wang, Qin Lu, Wenjie Li
Venue: WWW
Year: 2011

Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency.
SemSearch'11: the 4th semantic search workshop
Authors: Thanh Tran, Peter Mika, Haofen Wang, Marko Grobelnik
Venue: WWW
Year: 2011

The use of semantics and semantic technologies for search and retrieval has attracted interests both from academia and industry in recent years. What is now commonly known as Semantic Search is in fact a broad field encompassing ideas and concepts from different areas, including Information Retrieval, Semantic Web and database. This is the fourth edition of the Semantic Search workshop which aims to bring together researchers and practitioners from various communities, to provide a forum for dissemination, discussion, and for the exchange and transfer of knowledge related to the use of semantics for search and retrieval. This year's workshop will continue to push and promote efforts towards an evaluation benchmark for Semantic Search systems.
Measuring a commercial content delivery network
Authors: Sipat Triukose, Zhihua Wen, Michael Rabinovich
Venue: WWW
Year: 2011

Content delivery networks (CDNs) have become a crucial part of the modern Web infrastructure. This paper studies the performance of the leading content delivery provider - Akamai. It measures the performance of the current Akamai platform and considers a key architectural question faced by both CDN designers and their prospective customers: whether the co-location approach to CDN platforms adopted by Akamai, which tries to deploy servers in numerous Internet locations, brings inherent performance benefits over a more consolidated data center approach pursued by other influential CDNs such as Limelight. We believe the methodology we developed for this study will be useful for other researchers in the CDN arena.
CATE: context-aware timeline for entity illustration
Authors: Tran Anh Tuan, Shady Elbassuoni, Nicoleta Preda, Gerhard Weikum
Venue: WWW
Year: 2011

Wikipedia has become one of the most authoritative information sources on the Web. Each article in Wikipedia provides a portrait of a certain entity. However, such a portrait is far from complete. An informative portrait of an entity should also reveal the context the entity belongs to. For example, for a person, major historical, political and cultural events that coincide with her life are important and should be included in that person's portrait. Similarly, the person's interactions with other people are also important. All this information should be summarized and presented in an appealing and interactive visual interface that enables users to quickly scan the entity's portrait.
Parallel boosted regression trees for web search ranking
Authors: Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin
Venue: WWW
Year: 2011

Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned web-search ranking - a domain notorious for very large data sets. In this paper, we propose a novel method for parallelizing the training of GBRT. Our technique parallelizes the construction of the individual regression trees and operates using the master-worker paradigm as follows. The data are partitioned among the workers. At each iteration, the worker summarizes its data-partition using histograms. The master processor uses these to build one layer of a regression tree, and then sends this layer to the workers, allowing the workers to build histograms for the next layer. Our algorithm carefully orchestrates overlap between communication and computation to achieve good performance.
Identifying primary content from web pages and its application to web search ranking
Authors: Srinivas Vadrevu, Emre Velipasaoglu
Venue: WWW
Year: 2011

Web pages are usually highly structured documents. In some documents, content with different functionality is laid out in blocks, some merely supporting the main discourse. In other documents, there may be several blocks of unrelated main content. Indexing a web page as if it were a linear document can cause problems because of the diverse nature of its content. If the retrieval function treats all blocks of the web page equally without attention to structure, it may lead to irrelevant query matches. In this paper, we describe how content quality of different blocks of a web page can be utilized to improve a retrieval function. Our method is based on segmenting a web page into semantically coherent blocks and learning a predictor of segment content quality. We also describe how to use segment content quality estimates as weights in the BM25F formulation. Experimental results show our method improves relevance of retrieved results by as much as 4.5 % compared to BM25F that treats the body of a web page as a single section, and by a larger margin of over 9 % for difficult queries.
A non-syntactic approach for text sentiment classification with stopwords
Authors: V. Suresh, Ashok Veilumuthu, Avanthi Krishnamurthy, C. E. Veni Madhavan, Kaushik Nath, Sunil Arvindam
Venue: WWW
Year: 2011

The present approach uses stopwords and the gaps that occur between successive stopwords -formed by contentwords- as features for sentiment classification.
Application of semantic web technologies for multimedia interpretation
Authors: Ruben Verborgh, Rik Van de Walle
Venue: WWW
Year: 2011

Despite numerous outstanding results, highly complex and specialized multimedia algorithms have not been able to fulfill the promise of fully automated multimedia interpretation. An essential problem is that they are insufficiently aware of the context they operate in. Algorithms that do take a form of context in consideration, often function in a domain-specific environment. The generic framework proposed in this paper stimulates algorithm collaboration on an interpretation task by continuously actualizing the context of the multimedia item under interpretation. Semantic Web knowledge, combined with reasoning methods, forms the corner stone of the integration of these various interacting agents. We believe that this framework will enable an advanced interpretation of multimedia data that goes beyond the capabilities of individual algorithms. A basic platform implementation already indicates the potential of the concept, clearing the path for even more complex interpretation scenarios.
Evaluation of valuable user generated content on social news web sites
Authors: Yana Volkovich, Andreas Kaltenbrunner
Venue: WWW
Year: 2011

Social news websites have gained significant popularity in the last few years. The participants of such websites are not only allowed to share news links but also to annotate, to evaluate and to comment them. To quantify interestingness and attractiveness of the user generated content in respect to the original link source we introduce the User Generated Content add-on (UGC+) index. Based on the definition of UGC+ we also propose a concept for comparing groups of links filtered by different properties, e.g. authorship or topic-categories. We apply the proposed measure on the Spanish Digg-clone Menéame.
Is pay-per-click efficient?: an empirical analysis of click values
Authors: Dakan Wang, Gang Wang, Pinyan Lu, Yajun Wang, Zheng Chen, Botao Amber Hu
Venue: WWW
Year: 2011

Current sponsored search auction adopts per-click bidding. It implicitly assumes that an advertiser treats all clicks to be equally valuable. This is not always true in real world situations. Clicks which lead to conversions are definitely more valuable than those fraudulent clicks. In this work, we use post-ad-click behavior to measure a click's value and empirically show that for an advertiser, values of different clicks are highly variant. Thus for many clicks, the advertiser's single bid does not reflect his true valuations. This indicates that the sponsored search system under PPC mechanism is not efficient, or does not always give a slot to the advertiser who needs it most.
Information spreading in context
Authors: Dashun Wang, Zhen Wen, Hanghang Tong, Ching-Yung Lin, Chaoming Song, Albert-László Barabási
Venue: WWW
Year: 2011

Information spreading processes are central to human interactions. Despite recent studies in online domains, little is known about factors that could affect the dissemination of a single piece of information. In this paper, we address this challenge by combining two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system. We find that the social and organizational context significantly impacts to whom and how fast people forward information. Yet the structures within spreading processes can be well captured by a simple stochastic branching model, indicating surprising independence of context. Our results build the foundation of future predictive models of information flow and provide significant insights towards design of communication platforms.
Scalable spatio-temporal knowledge harvesting
Authors: Yafang Wang, Bin Yang, Spyros Zoupanos, Marc Spaniol, Gerhard Weikum
Venue: WWW
Year: 2011

Knowledge harvesting enables the automated construction of large knowledge bases. In this work, we made a first attempt to harvest spatio-temporal knowledge from news archives to construct trajectories of individual entities for spatio-temporal entity tracking. Our approach consists of an entity extraction and disambiguation module and a fact generation module which produce pertinent trajectory records from textual sources. The evaluation on the 20 years' New York Times news article corpus showed that our methods are effective and scalable.
Search result diversity for informational queries
Authors: Michael J. Welch, Junghoo Cho, Christopher Olston
Venue: WWW
Year: 2011

Ambiguous queries constitute a significant fraction of search instances and pose real challenges to web search engines. With current approaches the top results for these queries tend to be homogeneous, making it difficult for users interested in less popular aspects to find relevant documents. While existing research in search diversification offers several solutions for introducing variety into the results, the majority of such work is predicated, implicitly or otherwise, on the assumption that a single relevant document will fulfill a user's information need, making them inadequate for many informational queries. In this paper we present a search-diversification algorithm particularly suitable for informational queries by explicitly modeling that the user may need more than one page to satisfy their need. This modeling enables our algorithm to make a well-informed tradeoff between a user's desire for multiple relevant documents, probabilistic information about an average user's interest in the subtopics of a multifaceted query, and uncertainty in classifying documents into those subtopics. We evaluate the effectiveness of our algorithm against commercial search engine results and other modern ranking strategies, demonstrating notable improvement in multiple document scenarios.
Growing parallel paths for entity-page discovery
Authors: Tim Weninger, Fabio Fumarola, Cindy Xide Lin, Rick Barber, Jiawei Han, Donato Malerba
Venue: WWW
Year: 2011

In this paper, we use the structural and relational information on the Web to find entity-pages. Specifically, given a Web site and an entity-page (e.g., department and faculty member homepage) we seek to find all of the entity-pages of the same type (e.g., all faculty members in the department). To do this, we propose a web structure mining method which grows parallel paths through the web graph and DOM trees. We show that by utilizing these parallel paths we can efficiently discover all entity-pages of the same type. Finally, we demonstrate the accuracy of our method with a case study on various domains.
Finding our way on the web: exploring the role of waypoints in search interaction
Authors: Ryen W. White, Adish Singla
Venue: WWW
Year: 2011

Information needs are rarely satisfied directly on search engine result pages. Searchers usually need to click through to search results (landing pages) and follow search trails beyond those pages to fulfill information needs. We use the term waypoints to describe pages visited by searchers between the trail origin (the landing page) and the trail destination. The role that waypoints play in search interaction is poorly understood yet can be vital in determining search success. In this poster we analyze log data to determine the arrangement and function of waypoints, and study how these are affected by variations in information goals. Our findings have implications for understanding search behavior and for the design of interactive search support based on waypoints.
Who says what to whom on twitter
Authors: Shaomei Wu, Jake M. Hofman, Winter A. Mason, Duncan J. Watts
Venue: WWW
Year: 2011

We study several longstanding questions in media communications research, in the context of the microblogging service Twitter, regarding the production, flow, and consumption of information. To do so, we exploit a recently introduced feature of Twitter known as "lists" to distinguish between elite users - by which we mean celebrities, bloggers, and representatives of media outlets and other formal organizations - and ordinary users. Based on this classification, we find a striking concentration of attention on Twitter, in that roughly 50% of URLs consumed are generated by just 20K elite users, where the media produces the most information, but celebrities are the most followed. We also find significant homophily within categories: celebrities listen to celebrities, while bloggers listen to bloggers etc; however, bloggers in general rebroadcast more information than the other categories. Next we re-examine the classical "two-step flow" theory of communications, finding considerable support for it on Twitter. Third, we find that URLs broadcast by different categories of users or containing different types of content exhibit systematically different lifespans. And finally, we examine the attention paid by the different user categories to different news topics.
An adaptive ontology-based approach to identify correlation between publications
Authors: Huayu Wu, Hideaki Takeda, Masahiro Hamasaki, Tok Wang Ling, Liang Xu
Venue: WWW
Year: 2011

In this paper, we propose an adaptive ontology-based approach for related paper identification, to meet most researchers' practical needs. By searching ontology, we can return a diverse set of papers that are explicitly and implicitly related to an input paper. Moreover, our approach does not rely on known ontology. Instead, we build and update ontology for a collection with any domain of interest. Being independent from known ontology, our approach is much more adaptive for different domains.
A probabilistic model for opinionated blog feed retrieval
Authors: Xueke Xu, Tao Meng, Xueqi Cheng, Yue Liu
Venue: WWW
Year: 2011

In this poster, we study the problem of Opinionated Blog Feed Retrieval which can be considered as a particular type of the faceted blog distillation introduced by TREC 2009. It is a task of finding blogs not only having a principle and recurring interest in a given topic but also having a clear inclination towards expressing opinions on it. We propose a novel probabilistic model for this task which combines its two factors, topical relevance and opinionatedness, in a unified probabilistic framework. Experiments conducted in the context of the TREC 2009 & 2010 Blog Track show the effectiveness of the proposed model.
A kernel approach to addressing term mismatch
Authors: Jun Xu, Wei Wu, Hang Li, Gu Xu
Venue: WWW
Year: 2011

This paper addresses the problem of dealing with term mismatch in web search using 'blending'. In blending, the input query as well as queries similar to it are used to retrieve documents, the ranking results of documents with respect to the queries are combined to generate a new ranking list. We propose a principled approach to blending, using a kernel method and click-through data. Our approach consists of three elements: a way of calculating query similarity using click-through data, a mixture model for combination of rankings using relevance, query similarity, and document similarity scores, and an algorithm for learning the weights of blending model based on the kernel method. Large scale experiments on web search and enterprise search data sets show that our approach can effectively solve term mismatch problem and significantly outperform the baseline methods of query expansion and heuristic blending.
Game theoretic models for social network analysis
Authors: Yadati Narahari, Ramasuri Narayanam
Venue: WWW
Year: 2011

The existing methods and techniques for social network analysis are inadequate to capture both the behavior (such as rationality and intelligence) of individuals and the strategic interactions that occur among these individuals. Game theory is a natural tool to overcome this inadequacy since it provides rigorous mathematical models of strategic interaction among autonomous, intelligent, and rational agents. Motivated by the above observation, this tutorial provides the conceptual underpinnings of the use of game theoretic models in social network analysis. In the first part of the tutorial, we provide rigorous foundations of relevant concepts in game theory and social network analysis. In the second part of the tutorial, we present a comprehensive study of four contemporary and pertinent problems in social networks: social network formation, determining in influential individuals for viral marketing, query incentive networks, and community detection.
A finegrained digestion of news webpages through Event Snippet Extraction
Authors: Rui Yan, Liang Kong, Yu Li, Yan Zhang, Xiaoming Li
Venue: WWW
Year: 2011

We describe a framework to digest news webpages in finer granularity: to extract event snippets from contexts. "Events" are atomic text snippets and a news article is constituted by more than one event snippet. Event Snippet Extraction (ESE) aims to mine these snippets out. The problem is important because its solutions may be applied to many information mining and retrieval tasks. The challenge is to exploit rich features to detect snippet boundaries, including various semantic, syntactic and visual features. We run experiments to present the effectiveness of our approaches.
Caching intermediate result of SPARQL queries
Authors: Mengdong Yang, Gang Wu
Venue: WWW
Year: 2011

The complexity and growing scale of RDF data has made data management back end the performance bottleneck of Semantic Web applications. Caching is one of the ways that could solve this problem. However, few existing research projects focus on caching in RDF data processing. We present an adaptive caching scheme that caches intermediate result of basic graph pattern SPARQL queries. Benchmark test results are provided to illustrate the effectiveness of our caching scheme.
Location relevance classification for travelogue digests
Authors: Mao Ye, Rong Xiao, Wang-Chien Lee, Xing Xie
Venue: WWW
Year: 2011

In this paper, we aim to develop a travelogue service to discover and convey various travelogue digests, in form of theme locations and geographical scope to their readers. In this service, theme locations in a travelogue are the core information to discover. Due to the inherent ambiguity of location relevance, we explore the textual (e.g., surrounding words) and geographical (e.g., geographical relationship among locations) features of locations to perform location relevance classification for theme location discovery. Finally, we conduct comprehensive experiments on collected travelogues to evaluate the performance of our location relevance classification technique and demonstrate the effectiveness of the travelogue service.