OpenRaiser/Intern-Atlas huggingface dataset:
Big data, lifelong machine learning and transfer learning
Authors: Qiang Yang
Venue: WSDM
Year: 2013

A major challenge in today's world is the Big Data problem, which manifests itself in Web and Mobile domains as rapidly changing and heterogeneous data streams. A data-mining system must be able to cope with the influx of changing data in a continual manner. This calls for Lifelong Machine Learning, which in contrast to the traditional one-shot learning, should be able to identify the learning tasks at hand and adapt to the learning problems in a sustainable manner. A foundation for lifelong machine learning is transfer learning, whereby knowledge gained in a related but different domain may be transferred to benefit learning for a current task. To make effective transfer learning, it is important to maintain a continual and sustainable channel in the life time of a user in which the data are annotated. In this talk, I outline the lifelong machine learning situations, give several examples of transfer learning and applications for lifelong machine learning, and discuss cases of successful extraction of data annotations to meet the Big Data challenge.
App recommendation: a contest between satisfaction and temptation
Authors: Peifeng Yin, Ping Luo, Wang-Chien Lee, Min Wang
Venue: WSDM
Year: 2013

Due to the huge and still rapidly growing number of mobile applications (apps), it becomes necessary to provide users an app recommendation service. Different from conventional item recommendation where the user interest is the primary factor, app recommendation also needs to consider factors that invoke a user to replace an old app (if she already has one) with a new app. In this work we propose an Actual- Tempting model that captures such factors in the decision process of mobile app adoption. The model assumes that each owned app has an actual satisfactory value and a new app under consideration has a tempting value. The former stands for the real satisfactory value the owned app brings to the user while the latter represents the estimated value the new app may seemingly have. We argue that the process of app adoption therefore is a contest between the owned apps' actual values and the candidate app's tempting value. Via the extensive experiments we show that the AT model performs significantly better than the conventional recommendation techniques such as collaborative filtering and content-based recommendation. Furthermore, the best recommendation performance is achieved when the AT model is combined with them.
LaFT-tree: perceiving the expansion trace of one's circle of friends in online social networks
Authors: Jun Zhang, Chaokun Wang, Jianmin Wang, Philip S. Yu
Venue: WSDM
Year: 2013

Many patterns have been discovered to explain and analyze how people make friends. Among them is the triadic closure, supported by the principle of the transitivity of friendship, which means for an individual the friends of her friend are more likely to become her new friends. However, people's motivations under this principle haven't been well studied, and it's still unknown that how this principle works in diverse situations.
Triplex transfer learning: exploiting both shared and distinct concepts for text classification
Authors: Fuzhen Zhuang, Ping Luo, Changying Du, Qing He, Zhongzhi Shi
Venue: WSDM
Year: 2013

Transfer learning focuses on the learning scenarios when the test data from target domains and the training data from source domains are drawn from similar but different data distributions with respect to the raw features. Along this line, some recent studies revealed that the high-level concepts, such as word clusters, could help model the differences of data distributions, and thus are more appropriate for classification. In other words, these methods assume that all the data domains have the same set of shared concepts, which are used as the bridge for knowledge transfer. However, in addition to these shared concepts, each domain may have its own distinct concepts. In light of this, we systemically analyze the high-level concepts, and propose a general transfer learning framework based on nonnegative matrix trifactorization, which allows to explore both shared and distinct concepts among all the domains simultaneously. Since this model provides more flexibility in fitting the data, it can lead to better classification accuracy. Moreover, we propose to regularize the manifold structure in the target domains to improve the prediction performances. To solve the proposed optimization problem, we also develop an iterative algorithm and theoretically analyze its convergence properties. Finally, extensive experiments show that the proposed model can outperform the baseline methods with a significant margin. In particular, we show that our method works much better for the more challenging tasks when there are distinct concepts in the data.
Exploration and mining of web repositories
Authors: Nan Zhang, Gautam Das
Venue: WSDM
Year: 2014

With the proliferation of very large data repositories hidden behind web interfaces, e.g., keyword search, form-like search and hierarchical/graph-based browsing interfaces for Amazon.com, eBay.com, etc., efficient ways of searching, exploring and/or mining such web data are of increasing importance.
On discovering non-obvious recommendations: using unexpectedness and neighborhood selection methods in collaborative filtering systems
Authors: Panagiotis Adamopoulos
Venue: WSDM
Year: 2014

This paper proposes a number of studies in order to move the field of recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy. We contribute to existing helpful but less explored recommendation strategies and propose new approaches targeting to more useful recommendations for both users and businesses. Working toward this direction, we discuss the studies we have conducted so far and present our future research plans. The overall goal of this research program is to expand our focus from even more accurate rating predictions toward a more holistic experience for the users, by providing them with non-obvious but high quality recommendations and avoiding the over-specialization and concentration bias problems. In particular, we propose a new probabilistic neighborhood-based method as an improvement of the standard $k$-nearest neighbors approach, alleviating some of the most common problems of collaborative filtering recommender systems, based on classical metrics of dispersion and diversity as well as some newly proposed metrics. Furthermore, we propose a concept of unexpectedness in recommender systems and operationalize it by suggesting various mechanisms for specifying the expectations of the users and proposing a recommendation method for providing the users with unexpected but high quality personalized recommendations that fairly match their interests. Besides, in order to generate utility-based recommendations for Massive Open Online Courses (MOOCs) that better serve the educational needs of students, we study the satisfaction of users with online courses vis-a-vis student retention. Finally, we summarize the conclusions of the conducted studies, discuss the limitations of our work and also outline the managerial implications of the proposed stream of research.
Behavioral data mining and network analysis in massive online games
Authors: Muhammad Aurangzeb Ahmad, Jaideep Srivastava
Venue: WSDM
Year: 2014

The last decade has been characterized by an explosion of social media in a variety of forms. Since the data is captured in digital form it has become possible for the first time study human behavior at a massive scale. Not only is it possible to address traditional questions in the social sciences regarding collective dynamics of human behaviors but it is also possible to study new types of human behaviors which have arisen as a result of usage of new mediums like twitter, YouTube, Facebook, one games etc. Each of these mediums has its respective limitations and affordances. Out of all these mediums the most complex and data rich medium is that of Massive Online Games (MOGs). MOGs refer to massive online persistent environments (World of Warcraft, EVE Online, EverQuest etc) shared by millions of people . In general these environments are characterized by a rich array of activities and social interactions with a wide array of behaviors e.g., cooperation, trade, quest, deceit, mentoring etc. Such environments allow one to study human behavior at a level of granularity where it was not possible to do so previously. Given the challenges associated with analyzing this type of data traditional techniques in data mining and social network analysis have to be extended with insights from the social sciences. The tutorial will cover predictive and generative models in the study of MOGs. Additionally we will cover some SNA techniques which are more appropriate for MOGs given the multi-dimensionality of the data (P*/ERGM Models, IR Based Network Analysis, Hypergrah based Techniques, Coextensive Social Networks etc). We also describe the various ways in which MOGs exhibit similarities to the real world e.g., economic behaviors, clandestine behaviors, mentoring etc).
Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising
Authors: Amr Ahmed, Abhimanyu Das, Alexander J. Smola
Venue: WSDM
Year: 2014

Many estimation tasks come in groups and hierarchies of related problems. In this paper we propose a hierarchical model and a scalable algorithm to perform inference for multitask learning. It infers task correlation and subtask structure in a joint sparse setting. Implementation is achieved by a distributed subgradient oracle and the successive application of prox-operators pertaining to groups and subgroups of variables. We apply this algorithm to conversion optimization in display advertising. Experimental results on over 1TB data for up to 1 billion observations and 1 million attributes show that the algorithm provides significantly better prediction accuracy while simultaneously beingefficiently scalable by distributed parameter synchronization.
Workshop on large-scale and distributed systems for information retrieval (LSDS-IR 2014)
Authors: Ismail Sengör Altingövde, Berkant Barla Cambazoglu, Craig Macdonald, Nicola Tonellotto
Venue: WSDM
Year: 2014

The LSDS-IR'14 workshop aims to bring together information retrieval practitioners from industry and 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 related to the efficiency of large scale and distributed information retrieval systems.
Scalable topic-specific influence analysis on microblogs
Authors: Bin Bi, Yuanyuan Tian, Yannis Sismanis, Andrey Balmin, Junghoo Cho
Venue: WSDM
Year: 2014

Social influence analysis on microblog networks, such as Twitter, has been playing a crucial role in online advertising and brand management. While most previous influence analysis schemes rely only on the links between users to find key influencers, they omit the important text content created by the users. As a result, there is no way to differentiate the social influence in different aspects of life (topics). Although a few prior works do support topic-specific influence analysis, they either separate the analysis of content from the analysis of network structure, or assume that content is the only cause of links, which is clearly an inappropriate assumption for microblog networks.
Learning social network embeddings for predicting information diffusion
Authors: Simon Bourigault, Cédric Lagnier, Sylvain Lamprier, Ludovic Denoyer, Patrick Gallinari
Venue: WSDM
Year: 2014

Analyzing and modeling the temporal diffusion of information on social media has mainly been treated as a diffusion process on known graphs or proximity structures. The underlying phenomenon results however from the interactions of several actors and media and is more complex than what these models can account for and cannot be explained using such limiting assumptions. We introduce here a new approach to this problem whose goal is to learn a mapping of the observed temporal dynamic onto a continuous space. Nodes participating to diffusion cascades are projected in a latent representation space in such a way that information diffusion can be modeled efficiently using a heat diffusion process. This amounts to learning a diffusion kernel for which the proximity of nodes in the projection space reflects the proximity of their infection time in cascades. The proposed approach possesses several unique characteristics compared to existing ones. Since its parameters are directly learned from cascade samples without requiring any additional information, it does not rely on any pre-existing diffusion structure. Because the solution to the diffusion equation can be expressed in a closed form in the projection space, the inference time for predicting the diffusion of a new piece of information is greatly reduced compared to discrete models. Experiments and comparisons with baselines and alternative models have been performed on both synthetic networks and real datasets. They show the effectiveness of the proposed method both in terms of prediction quality and of inference speed.
Scalable K-Means by ranked retrieval
Authors: Andrei Z. Broder, Lluis Garcia Pueyo, Vanja Josifovski, Sergei Vassilvitskii, Srihari Venkatesan
Venue: WSDM
Year: 2014

The k-means clustering algorithm has a long history and a proven practical performance, however it does not scale to clustering millions of data points into thousands of clusters in high dimensional spaces. The main computational bottleneck is the need to recompute the nearest centroid for every data point at every iteration, aprohibitive cost when the number of clusters is large. In this paper we show how to reduce the cost of the k-means algorithm by large factors by adapting ranked retrieval techniques. Using a combination of heuristics, on two real life data sets the wall clock time per iteration is reduced from 445 minutes to less than 4, and from 705 minutes to 1.4, while the clustering quality remains within 0.5% of the k-means quality.
A few good predictions: selective node labeling in a social network
Authors: Gaurish Chaudhari, Vashist Avadhanula, Sunita Sarawagi
Venue: WSDM
Year: 2014

Many social network applications face the following problem: given a network G=(V,E) with labels on a small subset O \subset V of nodes and an optional set of features on nodes and edges, predict the labels of the remaining nodes. Much research has gone into designing learning models and inference algorithms for accurate predictions in this setting. However, a core hurdle to any prediction effort is that for many nodes there is insufficient evidence for inferring a label.
Effective co-betweenness centrality computation
Authors: Mostafa Haghir Chehreghani
Venue: WSDM
Year: 2014

Betweenness centrality of vertices is essential in the analysis of social and information networks, and co-betweenness centrality is one of two natural ways to extend it to sets of vertices. Existing algorithms for co-betweenness centrality computation suffer from at least one of the following problems: i) their applicability is limited to special cases like sequences, sets of size two, and ii) they are not efficient in terms of time complexity. In this paper, we present efficient algorithms for co-betweenness centrality computation of any set or sequence of vertices in weighted and unweighted networks. We also develop effective methods for co-betweenness centrality computation of sets and sequences of edges. These results provide a clear and extensive view about the complexity of co-betweenness centrality computation for vertices and edges in weighted and un-weighted networks. Finally, we perform extensive experiments on real-world networks from different domains including social, information and communication networks, to show the empirical efficiency of the proposed methods.
Understanding and promoting micro-finance activities in Kiva.org
Authors: Jaegul Choo, Changhyun Lee, Daniel Lee, Hongyuan Zha, Haesun Park
Venue: WSDM
Year: 2014

Non-profit Micro-finance organizations provide loaning opportunities to eradicate poverty by financially equipping impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to those who have little. Kiva.org, a widely-used crowd-funded micro-financial service, provides researchers with an extensive amount of publicly available data containing a rich set of heterogeneous information regarding micro-financial transactions. Our objective in this paper is to identify the key factors that encourage people to make micro-financing donations, and ultimately, to keep them actively involved. In our contribution to further promote a healthy micro-finance ecosystem, we detail our personalized loan recommendation system which we formulate as a supervised learning problem where we try to predict how likely a given lender will fund a new loan. We construct the features for each data item by utilizing the available connectivity relationships in order to integrate all the available Kiva data sources. For those lenders with no such relationships, e.g., first-time lenders, we propose a novel method of feature construction by computing joint nonnegative matrix factorizations. Utilizing gradient boosting tree methods, a state-of-the-art prediction model, we are able to achieve up to 0.92 AUC (area under the curve) value, which shows the potential of our methods for practical deployment. Finally, we point out several interesting phenomena on lenders' social behaviors in micro-finance activities.
Data design for personalization: current challenges and emerging opportunities
Authors: Elizabeth F. Churchill, Atish Das Sarma
Venue: WSDM
Year: 2014

There are several definitions of personalization but one that relates specifically to internet technologies is the following: Personalization technology enables the dynamic insertion, customization or suggestion of content in any format that is relevant to the individual user, based on the user's implicit behavior and preferences, and explicitly given details.
1st workshop on diffusion networks and cascade analytics
Authors: Peng Cui, Fei Wang, Hanghang Tong, Manuel Gomez-Rodriguez
Venue: WSDM
Year: 2014

Diffusion and cascades have been studied for many years in sociology, and different theoretical models have been developed. However, experimental validation has been always carried out in relatively small datasets. In recent years, with the availability of large-scale network and cascade data, research on cascading and diffusion phenomena has aroused considerable interests from various fields in computer science. One of the main goals is to discover different propagation patterns from historical cascade data. In this context, understanding the mechanisms underlying diffusion in both micro- and macro-scale levels and further develop predictive model of diffusion are fundamental problems of crucial importance.
Going beyond Corr-LDA for detecting specific comments on news & blogs
Authors: Mrinal Kanti Das, Trapit Bansal, Chiranjib Bhattacharyya
Venue: WSDM
Year: 2014

Understanding user generated comments in response to news and blog posts is an important area of research. After ignoring irrelevant comments, one finds that a large fraction, approximately 50%, of the comments are very specific and can be further related to certain parts of the article instead of the entire story. For example, in a recent product review of Google Nexus 7 in ArsTechnica (a popular blog), the reviewer talks about the prospect of "Retina equipped iPad mini" in a few sentences. It is interesting that although the article is on Nexus 7, but a significant number of comments are focused on this specific point regarding "iPad". We pose the problem of detecting such comments as specific comments location (SCL) problem. SCL is an important open problem with no prior work. SCL can be posed as a correspondence problem between comments and the parts of the relevant article, and one could potentially use Corr-LDA type models. Unfortunately, such models do not give satisfactory performance as they are restricted to using a single topic vector per article-comments pair. In this paper we propose to go beyond the single topic vector assumption and propose a novel correspondence topic model, namely SCTM, which admits multiple topic vectors (MTV) per article-comments pair. The resulting inference problem is quite complicated because of MTV and has no off-the-shelf solution. One of the major contributions of this paper is to show that using stick-breaking process as a prior over MTV, one can derive a collapsed Gibbs sampling procedure, which empirically works well for SCL.
Modeling opinion dynamics in social networks
Authors: Abhimanyu Das, Sreenivas Gollapudi, Kamesh Munagala
Venue: WSDM
Year: 2014

Our opinions and judgments are increasingly shaped by what we read on social media -- whether they be tweets and posts in social networks, blog posts, or review boards. These opinions could be about topics such as consumer products, politics, life style, or celebrities. Understanding how users in a network update opinions based on their neighbor's opinions, as well as what global opinion structure is implied when users iteratively update opinions, is important in the context of viral marketing and information dissemination, as well as targeting messages to users in the network.
Exploiting user disagreement for web search evaluation: an experimental approach
Authors: Thomas Demeester, Robin Aly, Djoerd Hiemstra, Dong Nguyen, Dolf Trieschnigg, Chris Develder
Venue: WSDM
Year: 2014

To express a more nuanced notion of relevance as compared to binary judgments, graded relevance levels can be used for the evaluation of search results. Especially in Web search, users strongly prefer top results over less relevant results, and yet they often disagree on which are the top results for a given information need. Whereas previous works have generally considered disagreement as a negative effect, this paper proposes a method to exploit this user disagreement by integrating it into the evaluation procedure. First, we present experiments that investigate the user disagreement. We argue that, with a high disagreement, lower relevance levels might need to be promoted more than in the case where there is global consensus on the top results. This is formalized by introducing the User Disagreement Model, resulting in a weighting of the relevance levels with a probabilistic interpretation. A validity analysis is given, and we explain how to integrate the model with well-established evaluation metrics. Finally, we discuss a specific application of the model, in the estimation of suitable weights for the combined relevance of Web search snippets and pages.
Integration of large scale knowledge bases using probabilistic graphical models
Authors: Arnab Kumar Dutta
Venue: WSDM
Year: 2014

Over the recent past, information extraction (IE) systems such as Nell and ReVerb have attained much success in creating large knowledge resources with minimal supervision. But, these resources in general, lack schema information and contain facts with high degree of ambiguity which are often difficult to interpret. Whereas, Wikipedia-based IE projects like DBpedia and Yago are structured, have disambiguated facts with unique identifiers and maintain a well-defined schema. In this work, we propose a probabilistic method to integrate these two types of IE projects where the structured knowledge bases benefit from the wide coverage of the semi-supervised IE projects and the latter benefits from the schema information of the former.