OpenRaiser/Intern-Atlas huggingface dataset:
Surviving a search engine overload
Authors: Aaron Koehl, Haining Wang
Venue: WWW
Year: 2012

Search engines are an essential component of the web, but their web crawling agents can impose a significant burden on heavily loaded web servers. Unfortunately, blocking or deferring web crawler requests is not a viable solution due to economic consequences. We conduct a quantitative measurement study on the impact and cost of web crawling agents, seeking optimization points for this class of request. Based on our measurements, we present a practical caching approach for mitigating search engine overload, and implement the two-level cache scheme on a very busy web server. Our experimental results show that the proposed caching framework can effectively reduce the impact of search engine overload on service quality.
Learning from positive and unlabeled amazon reviews: towards identifying trustworthy reviewers
Authors: Marios Kokkodis
Venue: WWW
Year: 2012

On-line marketplaces have been growing in importance over the last few years. In such environments, reviews consist the main reputation mechanism for the available products. Hence, presenting high quality reviews is crucial in achieving a high level of customer satisfaction. Towards this direction, in this work, we introduce a new dimension of review quality, the reviewer's "trustfulness". We assume that voluntary information provided by Amazon reviewers, regarding whether they are the actual buyers of the product, signals the reliability of a review. Based on this information, we characterize a reviewer as trustworthy (positive instance) or of unknown "trustfulness" (unlabeled instance). Then, we build models that exploit reviewers' profile information and on-line behavior to rank them according to the probability of being trustworthy. Our results are very promising, since they provide evidence that our predictive models separate positive from unlabeled instances with very high accuracies.
Harnessing user library statistics for research evaluation and knowledge domain visualization
Authors: Peter Kraker, Christian Körner, Kris Jack, Michael Granitzer
Venue: WWW
Year: 2012

Social reference management systems provide a wealth of information that can be used for the analysis of science. In this paper, we examine whether user library statistics can produce meaningful results with regards to science evaluation and knowledge domain visualization. We are conducting two empirical studies, using a sample of library data from Mendeley, the world's largest social reference management system. Based on the occurrence of references in users' libraries, we perform a large-scale impact factor analysis and an exploratory co-readership analysis. Our preliminary findings indicate that the analysis of user library statistics can produce accurate, timely, and content-rich results. We find that there is a significant relationship between the impact factor and the occurrence of references in libraries. Using a knowledge domain visualization based on co-occurrence measures, we are able to identify two areas of topics within the emerging field of technology-enhanced learning.
Visual oXPath: robust wrapping by example
Authors: Jochen Kranzdorf, Andrew Jon Sellers, Giovanni Grasso, Christian Schallhart, Tim Furche
Venue: WWW
Year: 2012

Good examples are hard to find, particularly in wrapper induction: Picking even one wrong example can spell disaster by yielding overgeneralized or overspecialized wrappers. Such wrappers extract data with low precision or recall, unless adjusted by human experts at significant cost.
Treehugger or petrolhead?: identifying bias by comparing online news articles with political speeches
Authors: Ralf Krestel, Alex Wall, Wolfgang Nejdl
Venue: WWW
Year: 2012

The Web is a very democratic medium of communication allowing everyone to express his or her opinion about any type of topic. This multitude of voices makes it more and more important to detect bias and help Internet users understand the background of information sources. Political bias of Web sites, articles, or blog posts is hard to identify straightaway. Manual content analysis conducted by experts is the standard way in political and social science to detect this bias. In this paper we present an automated approach relying on methods from information retrieval and corpus statistics to identify biased vocabulary use. As an example, we analyzed 15 years of parliamentary speeches of the German Bundestag and we investigated whether there is bias towards a political party in major national online newspapers and magazines. The results show that bias exists with respect to vocabulary use and it coincides with human judgement.
Towards optimizing the non-functional service matchmaking time
Authors: Kyriakos Kritikos, Dimitris Plexousakis
Venue: WWW
Year: 2012

The Internet is moving fast to a new era where million of services and things will be available. In this way, as there will be many functionally-equivalent services for a specific user task, the service non-functional aspect should be considered for filtering and choosing the appropriate services. The related approaches in service discovery mainly concentrate on exploiting constraint solving techniques for inferring if the user non-functional requirements are satisfied by the service nonfunctional capabilities. However, as the matchmaking time is proportional to the number of non-functional service descriptions, these approaches fail to fulfill the user request in a timely manner. To this end, two alternative techniques for improving the non-functional service matchmaking time have been developed. The first one is generic as it can handle non-functional service specifications containing n-ary constraints, while the second is only applicable to unary-constrained specifications. Both techniques were experimentally evaluated. The preliminary evaluation results show that the service matchmaking time is significantly improved without compromising matchmaking accuracy.
A semantic policy sharing and adaptation infrastructure for pervasive communities
Authors: Vikash Kumar
Venue: WWW
Year: 2012

Rule based information processing has traditionally been vital in many aspects of business, process manufacturing and information science. The need for rules gets even more magnified when limitations of ontology development in OWL are taken into account. In conjunction, the potent combination of ontology and rule based applications could be the future of information processing and knowledge representation on the web. However, semantic rules tend to be very dependent on multitudes of parameters and context data making it less flexible for use in applications where users could benefit from each other by socially sharing intelligence in the form of policies. This work aims to address this issue arising in rule based semantic applications in the use cases of smart home communities and privacy aware m-commerce setting for mobile users. In this paper, we propose a semantic policy sharing and adaptation infrastructure that enables a semantic rule created in one set of environmental, physical and contextual settings to be adapted for use in a situation when those settings/parameters/context variables change. The focus will mainly be on behavioural policies in the smart home use case and privacy enforcing and data filtering policies in the m-commerce scenario. Finally, we look into the possibility of making this solution application independent so that the benefits of such a policy adaptation infrastructure could be exploited in other application settings as well.
Adding wings to red bull media: search and display semantically enhanced video fragments
Authors: Thomas Kurz, Sebastian Schaffert, Georg Güntner, Manuel Fernandez
Venue: WWW
Year: 2012

The Linked Data movement with the aims of publishing and interconnecting machine readable data has originated in the last decade. Although the set of (open) data sources is rapidly growing, the integration of multimedia in this Web of Data is still at a very early stage. This paper describes, how arbitrary video content and metadata can be processed to identify meaningful linking partners for video fragments - and thus create a web of linked media. The video test-set for our demonstrator is part of the Red Bull Content Pool and confined to the Cliff Diving domain. The candidate set of possible link targets is a combination of a Red Bull thesaurus, information about divers from www.redbull.com and concepts from DBPedia. The demo includes both a semantic search on videos and video fragments and a player for videos with semantic enhancements.
Measuring usefulness of context for context-aware ranking
Authors: Andrey Kustarev, Yury Ustinovsky, Pavel Serdyukov
Venue: WWW
Year: 2012

Most of major search engines develop different types of personalisation of search results. Personalisation includes deriving user's long-term preferences, query disambiguation etc. User sessions provide very powerful tool commonly used for these problems. In this paper we focus on personalisation based on context-aware reranking. We implement a machine learning framework to approach this problem and study importance of different types of features. We stress that features concerning temporal and context relatedness of queries along with features relied on user's actions are most important and play crucial role for this type of personalisation.
Emails as graph: relation discovery in email archive
Authors: Michal Laclavik, Stefan Dlugolinský, Martin Seleng, Marek Ciglan, Ladislav Hluchý
Venue: WWW
Year: 2012

In this paper, we present an approach for representing an email archive in the form of a network, capturing the communication among users and relations among the entities extracted from the textual part of the email messages. We showcase the method on the Enron email corpus, from which we extract various entities and a social network. The extracted named entities (NE), such as people, email addresses and telephone numbers, are organized in a graph along with the emails in which they were found. The edges in the graph indicate relations between NEs and represent a co-occurrence in the same email part, paragraph, sentence or a composite NE. We study mathematical properties of the graphs so created and describe our hands-on experience with the processing of such structures. Enron Graph corpus contains a few million nodes and is large enough for experimenting with various graph-querying techniques, e.g. graph traversal or spread of activation. Due to its size, the exploitation of traditional graph processing libraries might be problematic as they keep the whole structure in the memory. We describe our experience with the management of such data and with the relation discovery among the extracted entities. The described experience might be valuable for practitioners and highlights several research challenges.
Kjing: (mix the knowledge)
Authors: Daniel Lacroix, Yves-Armel Martin
Venue: WWW
Year: 2012

Kjing is a web app that allow to rapidly set a multiscreen multi-device environment and to interact and distribute content in realtime. It can be used for museographic, educational or conferencing purpose.
TEM: a novel perspective to modeling content onmicroblogs
Authors: Himabindu Lakkaraju, Hyung-Il Ahn
Venue: WWW
Year: 2012

In recent times, microblogging sites like Facebook and Twitter have gained a lot of popularity. Millions of users world wide have been using these sites to post content that interests them and also to voice their opinions on several current events. In this paper, we present a novel non-parametric probabilistic model - Temporally driven Theme Event Model (TEM) for analyzing the content on microblogs. We also describe an online inference procedure for this model that enables its usage on large scale data. Experimentation carried out on real world data extracted from Facebook and Twitter demonstrates the efficacy of the proposed approach.
Handling forecast errors while bidding for display advertising
Authors: Kevin J. Lang, Benjamin Moseley, Sergei Vassilvitskii
Venue: WWW
Year: 2012

Most of the online advertising today is sold via an auction, which requires the advertiser to respond with a valid bid within a fraction of a second. As such, most advertisers employ bidding agents to submit bids on their behalf. The architecture of such agents typically has (1) an offline optimization phase which incorporates the bidder's knowledge about the market and (2) an online bidding strategy which simply executes the offline strategy. The online strategy is typically highly dependent on both supply and expected price distributions, both of which are forecast using traditional machine learning methods. In this work we investigate the optimum strategy of the bidding agent when faced with incorrect forecasts. At a high level, the agent can invest resources in improving the forecasts, or can tighten the loop between successive offline optimization cycles in order to detect errors more quickly. We show analytically that the latter strategy, while simple, is extremely effective in dealing with forecast errors, and confirm this finding with experimental evaluations.
A generic graph-based multidimensional recommendation framework and its implementations
Authors: Sangkeun Lee
Venue: WWW
Year: 2012

As the volume of information on the Web is explosively growing, recommender systems have become essential tools for helping users to find what they need or prefer. Most existing systems are two-dimensional in that they only exploit User and Item dimensions and perform a typical form of recommendation 'Recommending Item to User'. Yet, in many applications, the capabilities of dealing with multidimensional information and of adapting to various forms of recommendation requests are very important. In this paper, we take a graph-based approach to accomplishing such requirements in recommender systems and present a generic graph-based multidimensional recommendation framework. Based on the framework, we propose two homogeneous graph-based and one heterogeneous graph-based multidimensional recommendation methods. We expect our approach will be useful for increasing recommendation performance and enabling flexibility of recommender systems so that they can incorporate various user intentions into their recommendation process. We present our research result that we have reached and discuss remaining challenges and future work.
Bursty event detection from text streams for disaster management
Authors: Sung Jun Lee, Sangjin Lee, Kwanho Kim, Jonghun Park
Venue: WWW
Year: 2012

In this paper, an approach to automatically identifying bursty events from multiple text streams is presented. We investigate the characteristics of bursty terms that appear in the documents generated from text streams, and incorporate those characteristics into a term weighting scheme that distinguishes bursty terms from other non-bursty terms. Experimental results based on the news corpus show that our approach outperforms the existing alternatives in extracting bursty terms from multiple text streams. The proposed research is expected to contribute to increasing the situational awareness of ongoing events particularly when a natural or economic disaster occurs.
Interactive hypervideo visualization for browsing behavior analysis
Authors: Luis A. Leiva, Roberto Vivó
Venue: WWW
Year: 2012

Processing web interaction data is known to be cumbersome and time-consuming. State-of-the-art web tracking systems usually allow replaying user interactions in the form of mouse tracks, a video-like visualization scheme, to engage practitioners in the analysis process. However, traditional online video inspection has not explored the full capabilities of hypermedia and interactive techniques. In this paper, we introduce a web-based tracking tool that generates interactive visualizations from users' activity. The system unobtrusively collects browser events derived from normal usage, offering a unified framework to inspect interaction data in several ways. We compare our approach to related work in the research community as well as in commercial systems, and describe how ours fits in a real-world scenario. This research shows that there is a wide range of applications where the proposed tool can assist the WWW community.
CloudSpeller: query spelling correction by using a unified hidden markov model with web-scale resources
Authors: Yanen Li, Huizhong Duan, ChengXiang Zhai
Venue: WWW
Year: 2012

Query spelling correction is an important component of modern search engines that can help users to express an information need more accurately and thus improve search quality. In this work we proposed and implemented an end-to-end speller correction system, namely CloudSpeller. The CloudSpeller system uses a Hidden Markov Model to effectively model major types of spelling errors in a unified framework, in which we integrate a large-scale lexicon constructed using Wikipedia, an error model trained from high confidence correction pairs, and the Microsoft Web N-gram service. Our system achieves excellent performance on two search query spelling correction datasets, reaching 0.960 and 0.937 F1 scores on the TREC dataset and the MSN dataset respectively.
Finding influential seed successors in social networks
Authors: Cheng-Te Li, Hsun-Ping Hsieh, Shou-De Lin, Man-Kwan Shan
Venue: WWW
Year: 2012

In a dynamic social network, nodes can be removed from the network for some reasons, and consequently affect the behaviors of the network. In this paper, we tackle the challenge of finding a successor node for each removed seed node to maintain the influence spread in the network. Given a social network and a set of seed nodes for influence maximization, who are the best successors to be transferred the jobs of initial influence propagation if some seeds are removed from the network. To tackle this problem, we present and discuss five neighborhood-based selection heuristics, including degree, degree discount, overlapping, community bridge, and community degree. Experiments on DBLP co-authorship network show the effectiveness of devised heuristics.
Analyzing and predicting question quality in community question answering services
Authors: Baichuan Li, Tan Jin, Michael R. Lyu, Irwin King, Barley Mak
Venue: WWW
Year: 2012

Users tend to ask and answer questions in community question answering (CQA) services to seek information and share knowledge. A corollary is that myriad of questions and answers appear in CQA service. Accordingly, volumes of studies have been taken to explore the answer quality so as to provide a preliminary screening for better answers. However, to our knowledge, less attention has so far been paid to question quality in CQA. Knowing question quality provides us with finding and recommending good questions together with identifying bad ones which hinder the CQA service. In this paper, we are conducting two studies to investigate the question quality issue. The first study analyzes the factors of question quality and finds that the interaction between askers and topics results in the differences of question quality. Based on this finding, in the second study we propose a Mutual Reinforcement-based Label Propagation (MRLP) algorithm to predict question quality. We experiment with Yahoo!~Answers data and the results demonstrate the effectiveness of our algorithm in distinguishing high-quality questions from low-quality ones.
Influence propagation and maximization for heterogeneous social networks
Authors: Cheng-Te Li, Shou-De Lin, Man-Kwan Shan
Venue: WWW
Year: 2012

Influence propagation and maximization is a well-studied problem in social network mining. However, most of the previous works focus only on homogeneous social networks where nodes and links are of single type. This work aims at defining information propagation for heterogeneous social networks (containing multiple types of nodes and links). We propose to consider the individual behaviors of persons to model the influence propagation. Person nodes possess different influence probabilities to activate their friends according to their interaction behaviors. The proposed model consists of two stages. First, based on the heterogeneous social network, we create a human-based influence graph where nodes are of human-type and links carry weights that represent how special the target node is to the source node. Second, we propose two entropy-based heuristics to identify the disseminators in the influence graph to maximize the influence spread. Experimental results show promising results for the proposed method.
GPU-based minwise hashing: GPU-based minwise hashing
Authors: Ping Li, Anshumali Shrivastava, Arnd Christian König
Venue: WWW
Year: 2012

Minwise hashing is a standard technique for efficient set similarity estimation in the context of search. The recent work of b-bit minwise hashing provided a substantial improvement by storing only the lowest b bits of each hashed value. Both minwise hashing and b-bit minwise hashing require an expensive preprocessing step for applying k (e.g., k=500) permutations on the entire data in order to compute k minimal values as the hashed data. In this paper, we developed a parallelization scheme using GPUs, which reduced the processing time by a factor of 20-80. Reducing the preprocessing time is highly beneficial in practice, for example, for duplicate web page detection (where minwise hashing is a major step in the crawling pipeline) or for increasing the testing speed of online classifiers (when the test data are not preprocessed).
Dynamic selection of activation targets to boost the influence spread in social networks
Authors: Cheng-Te Li, Man-Kwan Shan, Shou-De Lin
Venue: WWW
Year: 2012

This paper aims to combine the viral marketing with the idea of direct selling to for influence maximization in a social network. In direct selling, producers can sell the products directly to the consumers without having to go through a cascade of wholesalers. Through direct selling, it is possible to sell the products in a more efficient and economic manner. Motivated by this idea, we propose a target-selecting independent cascade (TIC) model, in which during influence propagation each active node can give up to attempt to influence some neighboring nodes, named victims, who could be hard to affect, and try to activate some of its friends of friends, termed destinations, who could have higher potential to increase the influence spread. Thus, the next question to ask is that given a social network and a set of seeds for influence propagation under TIC model, how to select targets (i.e., victims and destinations) for the attempts of activation during the propagation to boost of influence spread. We propose and evaluate three heuristics for the target selection. Experiments show that selecting targets based on influence probability between nodes have the highest boost of influence spread.
Regional subgraph discovery in social networks
Authors: Cheng-Te Li, Man-Kwan Shan, Shou-De Lin
Venue: WWW
Year: 2012

This paper solves a region-based subgraph discovery problem. We are given a social network and some sample nodes which is supposed to belong to a specific region, and the goal is to obtain a subgraph that contains the sampled nodes with other nodes in the same region. Such regional subgraph discovery can benefit region-based applications, including scholar search, friend suggestion, and viral marketing. To deal with this problem, we assume there is a hidden backbone connecting the query nodes directly or indirectly in their region. The idea is that individuals belonging to the same region tend to share similar interests and cultures. By modeling such fact on edge weights, we search the graph to extract the regional backbone with respect to the query nodes. Then we can expand the backbone to derive the regional network. Experiments on a DBLP co-authorship network show the proposed method can effectively discover the regional subgraph with high precision scores.