OpenRaiser/Intern-Atlas huggingface dataset:
Document selection for tiered indexing in commerce search
Authors: Debmalya Panigrahi, Sreenivas Gollapudi
Venue: WSDM
Year: 2013

A search engine aims to return a set of relevant documents in response to a query, while minimizing the response time. This has led to the use of a tiered index, where the search engine maintains a small cache of documents that can serve a large fraction of queries. We give a novel algorithm for the selection of documents in a tiered index for commerce search (i.e. users searching for products on the web) that effectively exploits the superior structural characteristics of commerce search queries. This is in sharp contrast to previous approaches to tiered indexing that were aimed at general web search where queries are typically unstructured. We theoretically analyze our algorithms and give performance guarantees even in worst-case scenarios. We then complement and strengthen our theoretical claims by performing exhaustive experiments on real-world commerce search data, and show that our algorithm outperforms state-of-the-art tiered indexing techniques that were developed for general web search.
Workshop on semantic personalized information management (SPIM'13)
Authors: Till Plumbaum, Ernesto William De Luca, Aldo Gangemi, Michael Hausenblas
Venue: WSDM
Year: 2013

The SPIM workshop focuses especially on people that are working on the social or semantic Web, machine learning, user modeling, recommender systems, information retrieval, semantic interaction, or their combination. The goal is to bring together researchers and practitioners to initiating discussions on the different requirements and challenges coming with the social and semantic Web for personalized information retrieval systems. The workshop aims at improving the exchange of ideas between the different research communities and practitioners involved in the research on semantic personalized information management.
Online community detection in social sensing
Authors: Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang
Venue: WSDM
Year: 2013

The proliferation of location and GPS data streams which are collected in a wide variety of participatory sensing applications has created numerous possibilities for analysis of the underlying patterns of activity. Typically, the spatio-temporal patterns arising from such activity can be analyzed in order to determine the latent community structure in the underlying data. In this paper, we will examine the problem of online community detection from the location data collected from such social sensing applications in real time. Such data brings numerous challenges associated with it, in that they can be of a relatively large scale, and can be extremely noisy from the perspective of both data representation and analysis. Furthermore, the community structure in the underlying data cannot be directly inferred from the shape of the underlying trajectories, since a considerable amount of variation may exist in terms of trajectories of individuals belonging to the same community. In this paper, we will design online algorithms for community detection in social sensing applications. Our algorithm uses a robust and efficiently updateable model with the use of Gibbs sampling, and we will show its effectiveness and efficiency for social sensing applications.
Mining the web to predict future events
Authors: Kira Radinsky, Eric Horvitz
Venue: WSDM
Year: 2013

We describe and evaluate methods for learning to forecast forthcoming events of interest from a corpus containing 22 years of news stories. We consider the examples of identifying significant increases in the likelihood of disease outbreaks, deaths, and riots in advance of the occurrence of these events in the world. We provide details of methods and studies, including the automated extraction and generalization of sequences of events from news corpora and multiple web resources. We evaluate the predictive power of the approach on real-world events withheld from the system.
Maguro, a system for indexing and searching over very large text collections
Authors: Knut Magne Risvik, Trishul M. Chilimbi, Henry Tan, Karthik Kalyanaraman, Chris Anderson
Venue: WSDM
Year: 2013

Maguro is a system for efficiently searching very large collections of text content of up to 1 trillion documents at low cost. Search engines span across content that is very dynamic and highly augmented with metadata to the tail content of the web. A long tail distribution of content calls for different trade-offs in the design space for good efficiency across the entire index range. Maguro is designed for the long tail of content with less dynamics and less metadata, but very good cost efficiency. Maguro is part of the serving stack in Bing and allows us to scale the index significantly better.
WSCD2013: workshop on web search click data 2013
Authors: Pavel Serdyukov, Georges Dupret, Nick Craswell
Venue: WSDM
Year: 2013

WSCD 2013 is the third workshop on Web Search Click Data, following WSCD 2009 and WSCD 2012. It is a forum for new research relating to Web search usage logs and for discussing desirable properties of publicly released search log datasets. Research relating to search logs has been hampered by the limited availability of click datasets. This workshop comes with a new dataset based on logged user search behaviour and an accompanying challenge to predict switches between search engines within a given search session.
Learning to rank for spatiotemporal search
Authors: Blake Shaw, Jon Shea, Siddhartha Sinha, Andrew Hogue
Venue: WSDM
Year: 2013

In this article we consider the problem of mapping a noisy estimate of a user's current location to a semantically meaningful point of interest, such as a home, restaurant, or store. Despite the poor accuracy of GPS on current mobile devices and the relatively high density of places in urban areas, it is possible to predict a user's location with considerable precision by explicitly modeling both places and users and by combining a variety of signals about a user's current context. Places are often simply modeled as a single latitude and longitude when in fact they are complex entities existing in both space and time and shaped by the millions of people that interact with them. Similarly, models of users reveal complex but predictable patterns of mobility that can be exploited for this task. We propose a novel spatial search algorithm that infers a user's location by combining aggregate signals mined from billions of foursquare check-ins with real-time contextual information. We evaluate a variety of techniques and demonstrate that machine learning algorithms for ranking and spatiotemporal models of places and users offer significant improvement over common methods for location search based on distance and popularity.
Studying inter-national mobility through IP geolocation
Authors: Bogdan State, Ingmar Weber, Emilio Zagheni
Venue: WSDM
Year: 2013

The increasing ubiquity of Internet use has opened up new avenues in the study of human mobility. Easily-obtainable geolocation data resulting from repeated logins to the same website offer the possibility of observing long-term patterns of mobility for a large number of individuals. We use data on the geographic locations from where over 100 million anonymized users log into Yahoo!~services to generate the first global map of short- and medium-term mobility flows. We develop a protocol to identify anonymized users who, over a one-year period, had spent more than 3 months in a different country from their stated country of residence ("migrants"), and users who spent less than a month in another country ("tourists"). We compute aggregate estimates of migration propensities between countries, as inferred from a user's location over the observed period. Geolocation data allow us to characterize also the pendularity of migration flows -- i.e., the extent to which migrants travel back and forth between their countries of origin and destination. We use data regarding visa regimes, colonial ties, geographic location and economic development to predict migration and tourism flows. Our analysis shows the persistence of traditional migration patterns as well as the emergence of new routes. Migrations tend to be more pendular between countries that are close to each other. We observe particularly high levels of pendularity within the European Economic Area, even after we control for distance and visa regimes. The dataset, methodology and results presented have important implications for the travel industry, as well as for several disciplines in social sciences, including geography, demography and the sociology of networks.
Information-theoretic measures of influence based on content dynamics
Authors: Greg Ver Steeg, Aram Galstyan
Venue: WSDM
Year: 2013

The fundamental building block of social influence is for one person to elicit a response in another. Researchers measuring a "response" in social media typically depend either on detailed models of human behavior or on platform-specific cues such as re-tweets, hash tags, URLs, or mentions. Most content on social networks is difficult to model because the modes and motivation of human expression are diverse and incompletely understood. We introduce content transfer, an information-theoretic measure with a predictive interpretation that directly quantifies the strength of the effect of one user's content on another's in a model-free way. Estimating this measure is made possible by combining recent advances in non-parametric entropy estimation with increasingly sophisticated tools for content representation. We demonstrate on Twitter data collected for thousands of users that content transfer is able to capture non-trivial, predictive relationships even for pairs of users not linked in the follower or mention graph. We suggest that this measure makes large quantities of previously under-utilized social media content accessible to rigorous statistical causal analysis.
Learning multiple-question decision trees for cold-start recommendation
Authors: Mingxuan Sun, Fuxin Li, Joonseok Lee, Ke Zhou, Guy Lebanon, Hongyuan Zha
Venue: WSDM
Year: 2013

For cold-start recommendation, it is important to rapidly profile new users and generate a good initial set of recommendations through an interview process --- users should be queried adaptively in a sequential fashion, and multiple items should be offered for opinion solicitation at each trial. In this work, we propose a novel algorithm that learns to conduct the interview process guided by a decision tree with multiple questions at each split. The splits, represented as sparse weight vectors, are learned through an L_1-constrained optimization framework. The users are directed to child nodes according to the inner product of their responses and the corresponding weight vector. More importantly, to account for the variety of responses coming to a node, a linear regressor is learned within each node using all the previously obtained answers as input to predict item ratings. A user study, preliminary but first in its kind in cold-start recommendation, is conducted to explore the efficient number and format of questions being asked in a recommendation survey to minimize user cognitive efforts. Quantitative experimental validations also show that the proposed algorithm outperforms state-of-the-art approaches in terms of both the prediction accuracy and user cognitive efforts.
Models and algorithms for social influence analysis
Authors: Jimeng Sun, Jie Tang
Venue: WSDM
Year: 2013

Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this tutorial, we survey the research on social influence analysis with a focus on the computational aspects. First, we introduce how to verify the existence of social influence in various social networks. Second, we present computational models for quantifying social influence. Third, we describe how social influence can help real applications. In particular, we will focus on opinion leader finding and influence maximization for viral marketing. Finally, we apply the selected algorithms of social influence analysis on different social network data, such as twitter, arnetminer data, weibo, and slashdot forum.
Estimating content concreteness for finding comprehensible documents
Authors: Shinya Tanaka, Adam Jatowt, Makoto P. Kato, Katsumi Tanaka
Venue: WSDM
Year: 2013

Document comprehensibility is one of key factors determining document quality and, in result, user's satisfaction. Relevant web pages are of little utility if they are incomprehensible or impose too much cognitive burden on readers. Traditional measures of text difficulty focus often on syntactic factors of text such as sentence length, word length, syllable count, or they utilize fixed list of common terms. However, document comprehensibility depends on many factors, of which concreteness and the ease of concept visualization are crucial ones. In this paper, we first propose a method for predicting the concreteness of terms using SVM regression. We then extend it to calculating document concreteness level. The experimental results indicate satisfactory accuracy in estimating both term and document concreteness as well as demonstrate positive correlation between the document concreteness and comprehensibility. Our ultimate goal is to enable comprehension-driven search, which will return both relevant and comprehensible results.
Workshop on large-scale and distributed systems for information retrieval (LSDS-IR 2013)
Authors: Nicola Tonellotto, Craig Macdonald, Ismail Sengör Altingövde
Venue: WSDM
Year: 2013

The LSDS-IR'13 workshop aims to bring together both information retrieval practitioners from industry, as well as academic researchers concerned with efficient and distributed IR systems. The workshop also welcomes contributions that propose different ways of leveraging diversity and multiplicity of resources available in distributed systems. The main goal of the workshop is to attract people from industry and academia to present and discuss ideas, problems and results in efficiency of large scale and distributed information retrieval systems, and to foster their participation to the WSDM conference.
Three findings regarding privacy online
Authors: Catherine Tucker
Venue: WSDM
Year: 2013

The Internet now enables firms to collect detailed and potentially intrusive data about their customers both easily and cheaply. I discuss three empirical results related to customer privacy-protection that is enacted in response to this change. 1) Privacy protection that focuses on obtaining consent appears to lead to less effective advertising. This is based on results from extensive empirical analysis into how effectiveness of online advertising changed in response to the implementation of the E-Privacy Directive in Europe. 2) Privacy protection which gives direct control over customers' privacy appears to enhance economic outcomes. This is based on a detailed case study of customer responsiveness to different forms of advertising in the wake of a change in Facebook privacy policies. 3) Restricting the length of time that potentially private data is stored appears to have little economic impact. This is based on empirical analysis of aggregate search behavior following a change in the length of time search engines were told they could store search engine query data in Europe.
Characterizing and supporting cross-device search tasks
Authors: Yu Wang, Xiao Huang, Ryen W. White
Venue: WSDM
Year: 2013

Web searchers frequently transition from desktop computers and laptops to mobile devices, and vice versa. Little is known about the nature of cross-device search tasks, yet they represent an important opportunity for search engines to help their users, especially those on the target (post-switch) device. For example, the search engine could save the current session and re-instate it post switch, or it could capitalize on down-time between devices to proactively re-trieve content on behalf of the searcher. In this paper, we present a log-based study to define and characterize cross-device search be-havior and predict the resumption of cross-device tasks. Using data from a large commercial search engine, we show that there are dis-cernible and noteworthy patterns of search behavior associated with device transitions. We also develop learned models for predicting task resumption on the target device using behavioral, topical, geo-spatial, and temporal features. Our findings show that our models can attain strong prediction accuracy and have direct implications for the development of tools to help people search more effectively in a multi-device world.
The virtual lab
Authors: Duncan J. Watts
Venue: WSDM
Year: 2013

The Internet and the Web have transformed society, spawning new industries, altering social and cultural practices, and challenging long-accepted notions of individual privacy, intellectual property, and national security. In this talk, I argue that social science is also being transformed. In particular, I describe how crowd sourcing sites like Amazon's Mechanical Turk are increasingly being used by researchers to create "virtual labs" in which they can conduct behavioral experiments on a scale and speed that would have been hard to imagine just a decade ago. To illustrate the point, I describe some recent experiments that showcase the advantages of virtual over traditional physical labs, as well as some of the limitations. I then discuss how this relatively new experimental capability may unfold in the near future, along with some implications for social and behavioral science.
Data-driven political science
Authors: Ingmar Weber, Ana-Maria Popescu, Marco Pennacchiotti
Venue: WSDM
Year: 2013

The tutorial will summarize the state-of-the art in the growing area of computational political science. Like many others, this research domain is being revolutionized by the availability of open, big data and the increasing reach and importance of social media. The surging interest on the part of the academic community is matched by intense efforts on the part of political campaigns to use online data in order to learn how to best disseminate information and reach the right potential donors or voters. In this context, a tutorial can summarize existing methods in a fascinating, high-interest area and allow participants with diverse backgrounds to get inspiration from the methods and problems studied. The tutorial will feature seminal research concerning (i) political polarization, (ii) election prediction and polling, and (iii) political campaigning and influence propagation. The goal is not only to familiarize attendees with ideas from related conferences such as WWW, ICWSM or CIKM, but also to present ideas and quantitative methods closer to political science such as Poole's and Rosenthal's NOMINATE score for a politician's political orientation.
Exploring structure and content on the web: extraction and integration of the semi-structured web
Authors: Tim Weninger, Jiawei Han
Venue: WSDM
Year: 2013

In this tutorial we view the World Wide Web as a type of massive, decentralized database. At present, this "Web database" is presented in a manner largely devoid of any consistent meaning or schema. That is not to say that Web-data lacks an underlying organization; in fact, most Web content is generated from an underlying schema-bound, or otherwise structured database. Information extraction is generally concerned with the reconciliation of unstructured or semi-structured Web content with the neatly structured database paradigm. With this Web-database in hand, researchers and practitioners have recently begun developing mechanisms which return structured results in response to an unstructured query. These new developments are a product of (1) record, list and table extraction from large numbers of semi-structured Web pages, (2) integration of these disparate extraction results into a consistent form, and (3) analysis of the newly extracted and integrated Web data.
Patent partner recommendation in enterprise social networks
Authors: Sen Wu, Jimeng Sun, Jie Tang
Venue: WSDM
Year: 2013

It is often challenging to incorporate users' interactions into a recommendation framework in an online model. In this paper, we propose a novel interactive learning framework to formulate the problem of recommending patent partners into a factor graph model. The framework involves three phases: 1) candidate generation, where we identify the potential set of collaborators; 2) candidate refinement, where a factor graph model is used to adjust the candidate rankings; 3) interactive learning method to efficiently update the existing recommendation model based on inventors' feedback. We evaluate our proposed model on large enterprise patent networks. Experimental results demonstrate that the recommendation accuracy of the proposed model significantly outperforms several baselines methods using content similarity, collaborative filtering and SVM-Rank. We also demonstrate the effectiveness and efficiency of the interactive learning, which performs almost as well as offline re-training, but with only 1 percent of the running time.
Collective inference for network data with copula latent markov networks
Authors: Rongjing Xiang, Jennifer Neville
Venue: WSDM
Year: 2013

The popularity of online social networks and social media has increased the amount of linked data available in Web domains. Relational and Gaussian Markov networks have both been applied successfully for classification in these relational settings. However, since Gaussian Markov networks model joint distributions over continuous label space, it is difficult to use them to reason about uncertainty in discrete labels. On the other hand, relational Markov networks model probability distributions over discrete label space, but since they condition on the graph structure, the marginal probability for an instance will vary based on the structure of the subnetwork observed around the instance. This implies that the marginals will not be identical across instances and can sometimes result in poor prediction performance. In this work, we propose a novel latent relational model based on copulas which allows use to make predictions in a discrete label space while ensuring identical marginals and at the same time incorporating some desirable properties of modeling relational dependencies in a continuous space. While copulas have recently been used for descriptive modeling, they have not been used for collective classification in large scale network data and the associated conditional inference problem has not been considered before. We develop an approximate inference algorithm, and demonstrate empirically that our proposed Copula Latent Markov Network models based on approximate inference outperform a number of competing relational classification models over a range of real-world relational classification tasks.