OpenRaiser/Intern-Atlas huggingface dataset:
Lessons from the journey: a query log analysis of within-session learning
Authors: Carsten Eickhoff, Jaime Teevan, Ryen White, Susan T. Dumais
Venue: WSDM
Year: 2014

The Internet is the largest source of information in the world. Search engines help people navigate the huge space of available data in order to acquire new skills and knowledge. In this paper, we present an in-depth analysis of sessions in which people explicitly search for new knowledge on the Web based on the log files of a popular search engine. We investigate within-session and cross-session developments of expertise, focusing on how the language and search behavior of a user on a topic evolves over time. In this way, we identify those sessions and page visits that appear to significantly boost the learning process. Our experiments demonstrate a strong connection between clicks and several metrics related to expertise. Based on models of the user and their specific context, we present a method capable of automatically predicting, with good accuracy, which clicks will lead to enhanced learning. Our findings provide insight into how search engines might better help users learn as they search.
Sampling dilemma: towards effective data sampling for click prediction in sponsored search
Authors: Jun Feng, Jiang Bian, Taifeng Wang, Wei Chen, Xiaoyan Zhu, Tie-Yan Liu
Venue: WSDM
Year: 2014

Precise prediction of the probability that users click on ads plays a key role in sponsored search. State-of-the-art sponsored search systems typically employ a machine learning approach to conduct click prediction. While paying much attention to extracting useful features and building effective models, previous studies have overshadowed seemingly less obvious but essentially important challenges in terms of data sampling. To fulfill the learning objective of click prediction, it is not only necessary to ensure that the sampled training data implies the similar input distribution compared with the real world one, but also to guarantee that the sampled training data yield the consistent conditional output distribution, i.e. click-through rate (CTR), with the real world data. However, due to the sparseness of clicks in sponsored search, it is a bit contradictory to address these two challenges simultaneously. In this paper, we first take a theoretical analysis to reveal this sampling dilemma, followed by a thorough data analysis which demonstrates that the straightforward random sampling method may not be effective to balance these two kinds of consistency in sampling dilemma simultaneously. To address this problem, we propose a new sampling algorithm which can succeed in retaining the consistency between the sampled data and real world in terms of both input distribution and conditional output distribution. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that this new sampling algorithm can effectively address the sampling dilemma. Further experiments illustrate that, by using the training data obtained by our new sampling algorithm, we can learn the model with much higher accuracy in click prediction.
Improving the efficiency of multi-site web search engines
Authors: Guillem Francès, Xiao Bai, Berkant Barla Cambazoglu, Ricardo Baeza-Yates
Venue: WSDM
Year: 2014

A multi-site web search engine is composed of a number of search sites geographically distributed around the world. Each search site is typically responsible for crawling and indexing the web pages that are in its geographical neighborhood. A query is selectively processed on a subset of search sites that are predicted to return the best-matching results. The scalability and efficiency of multi-site web search engines have attracted a lot of research attention in recent years. In particular, research has focused on replicating important web pages across sites, forwarding queries to relevant sites, and caching results of previous queries. Yet, these problems have only been studied in isolation, but no prior work has properly investigated the interplay between them.
A self-adapting latency/power tradeoff model for replicated search engines
Authors: Ana Freire, Craig Macdonald, Nicola Tonellotto, Iadh Ounis, Fidel Cacheda
Venue: WSDM
Year: 2014

For many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power consumption of a replicated search engine can be automatically reduced when the system has low contention, without compromising its efficiency. We propose a novel self-adapting model to analyse the trade-off between latency and power consumption for distributed search engines. When query volumes are high and there is contention for the resources, the model automatically increases the necessary number of active machines in the system to maintain acceptable query response times. On the other hand, when the load of the system is low and the queries can be served easily, the model is able to reduce the number of active machines, leading to power savings. The model bases its decisions on examining the current and historical query loads of the search engine. Our proposal is formulated as a general dynamic decision problem, which can be quickly solved by dynamic programming in response to changing query loads. Thorough experiments are conducted to validate the usefulness of the proposed adaptive model using historical Web search traffic submitted to a commercial search engine. Our results show that our proposed self-adapting model can achieve an energy saving of 33% while only degrading mean query completion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic.
Democracy is good for ranking: towards multi-view rank learning and adaptation in web search
Authors: Wei Gao, Pei Yang
Venue: WSDM
Year: 2014

Web search ranking models are learned from features originated from different views or perspectives of document relevancy, such as query dependent or independent features. This seems intuitively conformant to the principle of multi-view approach that leverages distinct complementary views to improve model learning. In this paper, we aim to obtain optimal separation of ranking features into non-overlapping subsets (i.e., views), and use such different views for rank learning and adaptation. We present a novel semi-supervised multi-view ranking model, which is then extended into an adaptive ranker for search domains where no training data exists. The core idea is to proactively strengthen view consistency (i.e., the consistency between different rankings each predicted by a distinct view-based ranker) especially when training and test data follow divergent distributions. For this purpose, we propose a unified framework based on listwise ranking scheme to mutually reinforce the view consistency of target queries and the appropriate weighting of source queries that act as prior knowledge. Based on LETOR and Yahoo Learning to Rank datasets, our method significantly outperforms some strong baselines including single-view ranking models commonly used and multi-view ranking models that do not impose view consistency on target data.
Exploratory search with semantic transformations using collaborative knowledge bases
Authors: Yegin Genc
Venue: WSDM
Year: 2014

Sometimes we search for simple facts. Other times we search for relationships between concepts. While existing information retrieval systems work well for simple searches, they are less satisfying for complex inquiries because of the ill-structured nature of many searches and the cognitive load involved in the search process. Search can be improved by leveraging the network of concepts that are maintained by collaborative knowledge bases such as Wikipedia. By treating exploratory search inquires as networks of concepts -- and then mapping documents to these concepts, exploratory search performance can be improved. This method is applied to an exploratory search task: given a journal abstract, abstracts are ranked based their relevancy to the seed abstract. The results show comparable relevancy scores to state of the art techniques while at the same time providing better diversity.
Sentiment analysis on evolving social streams: how self-report imbalances can help
Authors: Pedro Henrique Calais Guerra, Wagner Meira Jr., Claire Cardie
Venue: WSDM
Year: 2014

Real-time sentiment analysis is a challenging machine learning task, due to scarcity of labeled data and sudden changes in sentiment caused by real-world events that need to be instantly interpreted. In this paper we propose solutions to acquire labels and cope with concept drift in this setting, by using findings from social psychology on how humans prefer to disclose some types of emotions. In particular, we use findings that humans are more motivated to report positive feelings rather than negative feelings and also prefer to report extreme feelings rather than average feelings.
Strategy in action: analyzing online search behavior bymining search strategies
Authors: Chathra Hendahewa
Venue: WSDM
Year: 2014

Analyzing people's Web search behavior has been a significant topic of interest in the Information Retrieval domain and search engine industry over the past decade. Research in this area has focused on improving search and retrieval capabilities leading to high demands and expectations of Web search users. Understanding and analyzing the Web search process when users are performing Web search tasks is a challenging problem due to many reasons such as subjectivity, dynamic nature, difficulty in measurement of success and difficulty in evaluation. I propose to analyze the users' Web search behavior in order to identify the strategies and tactics they use in fulfilling their task. In order to achieve this, I intend to use data mining and machine learning methods with an emphasis on time series analysis given that the user search process can be considered as a sequence of time related events.
Who likes it more?: mining worth-recommending items from long tails by modeling relative preference
Authors: Yu-Chieh Ho, Yi-Ting Chiang, Jane Yung-jen Hsu
Venue: WSDM
Year: 2014

Recommender systems are useful tools that help people to filter and explore massive information. While the accuracy of recommender systems is important, many recent research indicated that focusing merely on accuracy not only is insufficient to meet user needs, but also may be harmful. Other characteristics such as novelty, unexpectedness and diversity should also be taken into consideration. Previous work has shown that more the sales of long-tail items could be more beneficial to both customers and some business models. However, the majority of collaborative filtering approaches tends to recommend popular selling items.
Social collaborative retrieval
Authors: Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III
Venue: WSDM
Year: 2014

Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval-a combination of these two traditional problems-has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset.
Big graph mining for the web and social media: algorithms, anomaly detection, and applications
Authors: U Kang, Leman Akoglu, Duen Horng Chau
Venue: WSDM
Year: 2014

Graphs are everywhere: social networks, computer net- works, mobile call networks, the World Wide Web, protein interaction networks, and many more. The lower cost of disk storage, the success of social networking websites and Web 2.0 applications, and the high availability of data sources lead to graphs being generated at unprecedented size. They are now measured in terabytes or even petabytes, with more than billions of nodes and edges. Finding patterns on large graphs have a lot of applica- tions including cyber security on the Web, social media min- ing (Facebook, Twitter), and fraud detection, among others. This tutorial will cover topics related to finding patterns and anomalies and sensemaking in large-scale graphs with appli- cations to real-world problems in social media and the Web. Specifically, we aim to answer the following questions: How can we scale up graph mining algorithms for massive graphs with billions of edges? How can we find anomalies in such large-scale graphs? How can we make sense of disk-resident large graphs, what and how can we do visual analytics? How can we use the algorithms and anomaly detection techniques to solve challenging real-world problems that play key role in social media and the Web? Our tutorial consists of three main parts. We start with scalable graph mining algorithms for billion-scale graphs, in- cluding structure analysis, eigensolvers, storage and index- ing, and graph layout and graph compression. Next we de- scribe anomaly detection techniques for large scale graphs with applications on social media. Finally, we discuss vi- sual analytics techniques which leverage these algorithms and anomaly detection techniques in the previous parts.
Supervised N-gram topic model
Authors: Noriaki Kawamae
Venue: WSDM
Year: 2014

We propose a Bayesian nonparametric topic model that rep- resents relationships between given labels and the corre- sponding words/phrases, from supervised articles. Unlike existing supervised topic models, our proposal, supervised N-gram topic model (SNT), focuses on both a number of topics and power-law distribution in the word frequencies to extract topic specific N-grams. To achieve this goal, SNT takes a Bayesian nonparametric approach to topic sampling, which generates word distribution jointly with the given variable in textual order, and then form each N-gram word as a hierarchy of Pitman-Yor process priors. Experiments on labeled text data show that SNT is useful as a generative model for discovering more phrases that complement human experts and domain specific knowledge than the existing al- ternatives. The results show that SNT can be applied to various tasks such as automatic annotation.
Visualizing brand associations from web community photos
Authors: Gunhee Kim, Eric P. Xing
Venue: WSDM
Year: 2014

Brand Associations, one of central concepts in marketing, describe customers' top-of-mind attitudes or feelings toward a brand. Thus, this consumer-driven brand equity often attains the grounds for purchasing products or services of the brand. Traditionally, brand associations are measured by analyzing the text data from consumers' responses to the survey or their online conversation logs. In this paper, we propose to go beyond text data and leverage large-scale online photo collections contributed by the general public, which have not been explored so far. As a first technical step toward the study of photo-based brand associations, we aim to jointly achieve the following two visualization tasks in a mutually-rewarding way: (i) detecting and visualizing core visual concepts associated with brands, and (ii) localizing the regions of brand in the images. With experiments on about five millions of images of 48 brands crawled from five popular online photo sharing sites, we demonstrate that our approach can discover complementary views on the brand associations that are hardly mined from the text data. We also quantitatively show that our approach outperforms other candidate methods on the both visualization tasks.
Discovering common motifs in cursor movement data for improving web search
Authors: Dmitry Lagun, Mikhail Ageev, Qi Guo, Eugene Agichtein
Venue: WSDM
Year: 2014

Web search behavior and interaction data, such as mouse cursor movements, can provide valuable information on how searchers examine and engage with the web search results. This interaction data is far richer than traditional search click data, and can be used to improve search ranking, evaluation, and presentation. Unfortunately, the diversity and complexity inherent in this interaction data make it more difficult to capture salient behavior characteristics through traditional feature engineering. To address this problem, we introduce a novel approach of automatically discovering frequent subsequences, or motifs, in mouse cursor movement data. In order to scale our approach to realistic datasets, we introduce novel optimizations for motif discovery, specifically designed for mining cursor movement data. As a practical application, we show that by encoding the motifs discovered from thousands of real web search sessions as features, enables significant improvements on result relevance estimation and re-ranking tasks, compared to a state-of-the-art baseline that relies on extensive feature engineering. These results, complemented with visualization and qualitative analysis, demonstrate that our approach is able to automatically capture key characteristics of mouse cursor movement behavior, providing a valuable new tool for search behavior analysis.
Nonparametric bayesian upstream supervised multi-modal topic models
Authors: Renjie Liao, Jun Zhu, Zengchang Qin
Venue: WSDM
Year: 2014

Learning with multi-modal data is at the core of many multimedia applications, such as cross-modal retrieval and image annotation. In this paper, we present a nonparametric Bayesian approach to learning upstream supervised topic models for analyzing multi-modal data. Our model develops a compound nonparametric Bayesian multi-modal prior to describe the correlation structure of data both within each individual modality and between different modalities. It extends the hierarchical Dirichlet process (HDP) through incorporating upstream supervised response variables and values of latent functions under Gaussian process (GP). Upstream responses shared by data from multiple modalities are beneficial for discriminatively training and GP allows flexible structure learning of correlations. Hence, our model inherits the automatic determination of the number of topics from HDP, structure learning from GP and enhanced predictive capacity from upstream supervision. We also provide efficient variational inference and prediction algorithms. Empirical studies demonstrate superior performances on several benchmark datasets compared with previous competitors.
Multilingual probabilistic topic modeling and its applications in web mining and search
Authors: Marie-Francine Moens, Ivan Vulic
Venue: WSDM
Year: 2014

Multilingual topic models are a fairly novel group of unsupervised, language-independent and generative machine learning models. This tutorial covers all key aspects of their probabilistic framework and demonstrates how to easily integrate these models into frameworks for cross-lingual and multilingual Web mining and search.
Using linked data to mine RDF from wikipedia's tables
Authors: Emir Muñoz, Aidan Hogan, Alessandra Mileo
Venue: WSDM
Year: 2014

The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods to recover the semantics of Wikipedia tables and, in particular, to extract facts from them in the form of RDF triples. Our core method uses an existing Linked Data knowledge-base to find pre-existing relations between entities in Wikipedia tables, suggesting the same relations as holding for other entities in analogous columns on different rows. We find that such an approach extracts RDF triples from Wikipedia's tables at a raw precision of 40%. To improve the raw precision, we define a set of features for extracted triples that are tracked during the extraction phase. Using a manually labelled gold standard, we then test a variety of machine learning methods for classifying correct/incorrect triples. One such method extracts 7.9 million unique and novel RDF triples from over one million Wikipedia tables at an estimated precision of 81.5%.
Data that matter: opportunities in crisis informatics research
Authors: Leysia Palen
Venue: WSDM
Year: 2014

In an increasingly global society and on a planet experiencing effects of climate change, large-scale emergencies both instigated by humans and arising from nature can devastate human life and our tightly- woven social fabric. With a promise of improved warning and coordination, a prevailing hope is that information and communication technology (ICT) can help reduce the impacts of large-scale disruptions, including political crises, natural disasters, pandemics, and terrorist threats. Much of the focus of development has been on the formal emergency response effort.
Web-scale classification: web classification in the big data era
Authors: Ioannis Partalas, Massih-Reza Amini, Ion Androutsopoulos, Thierry Artières, Patrick Gallinari, Éric Gaussier, Georgios Paliouras
Venue: WSDM
Year: 2014

This paper provides an overview of the workshop Web-Scale Classification: Web Classification in the Big Data Era which was held in New York City, on February 28th as a workshop of the seventh International Conference on Web Search and Data Mining. The goal of the workshop was to discuss and assess recent research focusing on classification and mining in Web-scale category systems. The workshop brought together members of several communities such web mining, machine learning, text classification and social media mining.
Modelling growth of urban crowd-sourced information
Authors: Giovanni Quattrone, Afra J. Mashhadi, Daniele Quercia, Chris Smith-Clarke, Licia Capra
Venue: WSDM
Year: 2014

Urban crowd-sourcing has become a popular paradigm to harvest spatial information about our evolving cities directly from citizens. OpenStreetMap is a successful example of such paradigm, with an accuracy of its user-generated content comparable to that of curated databases (e.g., Ordnance Survey). Coverage is however low and most importantly non-uniformly distributed across the city. Being able to model the spontaneous growth of digital information in these domains is required, so to be able to plan interventions aimed at gathering content about areas that would otherwise be neglected. Inspired by models of physical urban growth developed by urban planners, we build a model of digital growth of crowd-sourced spatial information that is both easy to interpret and dynamic, so to be able to determine what factors impact growth and how these change over time. We build and test the model against five years of OpenStreetMap data for the city of London, UK. We then run the model against two other cities, chosen for their different physical and digital growth's characteristics, so to stress-test the model. We conclude with a discussion of the implications of this work on both developers and users of urban crowd-sourcing applications.