OpenRaiser/Intern-Atlas huggingface dataset:
"Which half Is wasted?": controlled experiments to measure online-advertising effectiveness
Authors: David H. Reiley
Venue: KDD
Year: 2011

The department-store retailer John Wanamaker famously stated, "Half the money I spend on advertising is wasted--I just don't know which half." Compared with the measurement of advertising effectiveness in traditional media, online advertisers and publishers have considerable data advantages, including individual-level data on advertising exposures, clicks, searches, and other online user behaviors. However, as I shall discuss in this talk, the science of advertising effectiveness requires more than just quantity of data - even more important is the quality of the data. In particular, in many cases, using various statistical techniques with observational data leads to incorrect measurements. To measure the true causal effects, we run controlled experiments that suppress advertising to a control group, much like the placebo in a drug trial. With experiments to determine the ground truth, we can show that in many circumstances, observational-data techniques rely on identifying assumptions that prove to be incorrect, and they produce estimates differing wildly from the truth. Despite increases in data availability, Wanamaker's complaint remains just as true for online advertising as it was for print advertising a century ago.
Knowledge discovery and data mining in pharmaceutical cancer research
Authors: Paul A. Rejto
Venue: KDD
Year: 2011

Biased and unbiased approaches to develop predictive biomarkers of response to drug treatment will be introduced and their utility demonstrated for cell cycle inhibitors. Opportunities to leverage the growing knowledge of tumors characterized by modern methods to measure DNA and RNA will be shown, including the use of appropriate preclinical models and selection of patients. Furthermore, techniques to identify mechanisms of resistance prior to clinical treatment will be discussed. Prospects for systematic data mining and current barriers to the application of precision medicine in cancer will be reviewed along with potential solutions.
Web information extraction using markov logic networks
Authors: Sandeepkumar Satpal, Sahely Bhadra, Sundararajan Sellamanickam, Rajeev Rastogi, Prithviraj Sen
Venue: KDD
Year: 2011

In this paper, we consider the problem of extracting structured data from web pages taking into account both the content of individual attributes as well as the structure of pages and sites. We use Markov Logic Networks (MLNs) to capture both content and structural features in a single unified framework, and this enables us to perform more accurate inference. MLNs allow us to model a wide range of rich structural features like proximity, precedence, alignment, and contiguity, using first-order clauses. We show that inference in our information extraction scenario reduces to solving an instance of the maximum weight subgraph problem. We develop specialized procedures for solving the maximum subgraph variants that are far more efficient than previously proposed inference methods for MLNs that solve variants of MAX-SAT. Experiments with real-life datasets demonstrate the effectiveness of our MLN-based approach compared to existing state-of-the-art extraction methods.
Exploiting place features in link prediction on location-based social networks
Authors: Salvatore Scellato, Anastasios Noulas, Cecilia Mascolo
Venue: KDD
Year: 2011

Link prediction systems have been largely adopted to recommend new friends in online social networks using data about social interactions. With the soaring adoption of location-based social services it becomes possible to take advantage of an additional source of information: the places people visit. In this paper we study the problem of designing a link prediction system for online location-based social networks. We have gathered extensive data about one of these services, Gowalla, with periodic snapshots to capture its temporal evolution. We study the link prediction space, finding that about 30% of new links are added among "place-friends", i.e., among users who visit the same places. We show how this prediction space can be made 15 times smaller, while still 66% of future connections can be discovered. Thus, we define new prediction features based on the properties of the places visited by users which are able to discriminate potential future links among them.
Discovering shakers from evolving entities via cascading graph inference
Authors: Xiaoxiao Shi, Wei Fan, Jianping Zhang, Philip S. Yu
Venue: KDD
Year: 2011

In an interconnected and dynamic world, the evolution of one entity may cause a series of significant value changes for some others. For example, the currency inflation of Thailand caused the currency slump of other Asian countries, which eventually led to the financial crisis of 1997. We call such high impact entities shakers. To discover shakers, we first introduce the concept of a cascading graph to capture the causality relationships among evolving entities over some period of time, and then infer shakers from the graph. In a cascading graph, nodes represent entities and weighted links represent the causality effects. In order to find hidden shakers in such a graph, two scoring functions are proposed, each of which estimates how much the target entity can affect the values of some others. The idea is to artificially inject a significant change on the target entity, and estimate its direct and indirect influence on the others, by following an inference rule under the Markovian assumption. Both scoring functions are proven to be only dependent on the structure of a cascading graph and can be calculated in polynomial time. Experiments included three datasets in social sciences. Without directly applicable previous methods, we modified three graphical models as baselines. The two proposed scoring functions can effectively capture those high impact entities. For example, in the experiment to discover stock market shakers, the proposed models outperform the three baselines by as much as 50% in accuracy with the ground truth obtained from Yahoo!~Finance.
A simple statistical model and association rule filtering for classification
Authors: György J. Simon, Vipin Kumar, Peter W. Li
Venue: KDD
Year: 2011

Associative classification is a predictive modeling technique that constructs a classifier based on class association rules (also known as predictive association rules; PARs). PARs are association rules where the consequence of the rule is a class label. Associative classification has gained substantial research attention because it successfully joins the benefits of association rule mining with classification. These benefits include the inherent ability of association rule mining to extract high-order interactions among the predictors--an ability that many modern classifiers lack--and also the natural interpretability of the individual PARs.
Understanding atrophy trajectories in alzheimer's disease using association rules on MRI images
Authors: György J. Simon, Peter W. Li, Clifford R. Jack Jr., Prashanthi Vemuri
Venue: KDD
Year: 2011

Alzheimer's disease (AD) is associated with progressive cognitive decline leading to dementia. The atrophy/loss of brain structure as seen on Magnetic Resonance Imaging (MRI) is strongly correlated with the severity of the cognitive impairment in AD. In this paper, we set out to find associations between predefined regions of the brain (regions of interest; ROIs) and the severity of the disease. Specifically, we use these associations to address two important issues in AD: (i) typical versus atypical atrophy patterns and (ii) the origin and direction of progression of atrophy, which is currently under debate.
Data intensive analysis on the gordon high performance data and compute system
Authors: Robert S. Sinkovits, Pietro Cicotti, Shawn Strande, Mahidhar Tatineni, Paul Rodríguez, Nicole Wolter, Natasha Balac
Venue: KDD
Year: 2011

The Gordon data intensive computing system was designed to handle problems with large memory requirements that cannot easily be solved using standard workstations or distributed memory supercomputers. We describe the unique features of Gordon that make it ideally suited for data mining and knowledge discovery applications: memory aggregation using the vSMP software solution from ScaleMP, I/O nodes containing 4 TB of low-latency flash memory, and a high performance parallel file system with 4 PB capacity. We also demonstrate how a number of standard data mining tools (e.g. Matlab, WEKA, R) can be used effectively on Dash, an early prototype of the full Gordon system.
Refining causality: who copied from whom?
Authors: Tristan Mark Snowsill, Nick Fyson, Tijl De Bie, Nello Cristianini
Venue: KDD
Year: 2011

Inferring causal networks behind observed data is an active area of research with wide applicability to areas such as epidemiology, microbiology and social science. In particular recent research has focused on identifying how information propagates through the Internet. This research has so far only used temporal features of observations, and while reasonable results have been achieved, there is often further information which can be used.
Broad scale predictive modeling and marketing optimization in retail sales
Authors: Dan Steinberg, Felipe Fernandez Martinez
Venue: KDD
Year: 2011

The challenge of predicting retail sales on a product-by-product basis throughout a network of retail stores has been researched intensively by applied econometricians and statisticians for decades. The principal tools of analysis have been linear regression with Bayesian inspired adjustments to stabilize demand curve estimates. The scale of such analytics can be challenging as retailers often work with more than 100,000 products (SKUs) and typically operate networks of hundreds of brick and mortar stores. Department and grocery stores are excellent examples but fast food restaurants also require such detailed predictive modeling systems. Depending on the objectives of the company, predictions may be required for blocks of time spanning a week or more, or, as in the case of fast food operators, predictions are required for each 15-minute time interval of the operating day. The authors have modernized industry standard approaches to such predictive modeling by leveraging advanced data mining techniques. These techniques are more adept in detecting nonlinear response and accommodating interactions and automatically sifting through hundreds if not thousands of potential factors influencing sales outcomes. Results show that conventional statistical models miss a substantial fraction of the explainable variance while the new methods dominate in terms of performance and speed of model development. Accurate prediction is required for reliable planning and logistics, and optimization. Optimization with respect to pricing, promotion and assortment can be asked for relative to a variety of objectives (e.g. revenue, profits) and short term and long-term optimization may result in different decisions being taken. A unique challenge for retailers is the large number of constraints to which complex retail organizations are subject. Contracts and special understandings with valued suppliers severely constrain a retailer's flexibility. For example, certain products may not be promotable (or discounted) in isolation, and others (say from competitors) may not be promoted jointly, and the costs of goods sold may well depend on the quantities contracted. We discuss how we have resolved such challenges via a cycle of prediction and simulation to develop a flexible high-speed system for handling arbitrary constraints, arbitrary objectives, and achieve new levels of predictive accuracy and reliability.
Privacy-preserving social network publication against friendship attacks
Authors: Chih-Hua Tai, Philip S. Yu, De-Nian Yang, Ming-Syan Chen
Venue: KDD
Year: 2011

Due to the rich information in graph data, the technique for privacy protection in published social networks is still in its infancy, as compared to the protection in relational databases. In this paper we identify a new type of attack called a friendship attack. In a friendship attack, an adversary utilizes the degrees of two vertices connected by an edge to re-identify related victims in a published social network data set. To protect against such attacks, we introduce the concept of k2-degree anonymity, which limits the probability of a vertex being re-identified to 1/k. For the k2-degree anonymization problem, we propose an Integer Programming formulation to find optimal solutions in small-scale networks. We also present an efficient heuristic approach for anonymizing large-scale social networks against friendship attacks. The experimental results demonstrate that the proposed approaches can preserve much of the characteristics of social networks.
Topic-level social network search
Authors: Jie Tang, Sen Wu, Bo Gao, Yang Wan
Venue: KDD
Year: 2011

We study the problem of topic-level social network search, which aims to find who are the most influential users in a network on a specific topic and how the influential users connect with each other. We employ a topic model to find topical aspects of each user and a retrieval method to identify influential users by combining the language model and the topic model. An influence maximization algorithm is then presented to find the sub network that closely connects the influential users. Two demonstration systems have been developed and are online available. Empirical analysis based on the user's viewing time and the number of clicks validates the proposed methodologies.
Diversified ranking on large graphs: an optimization viewpoint
Authors: Hanghang Tong, Jingrui He, Zhen Wen, Ravi B. Konuru, Ching-Yung Lin
Venue: KDD
Year: 2011

Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications. There are two important open questions here. The first challenge is the measure - how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity? The second challenge lies in the algorithmic aspect - how to find an optimal, or near-optimal, top-k ranking list that maximizes the measure we defined in a scalable way? In this paper, we address these challenges from an optimization point of view. Firstly, we propose a goodness measure for a given top-k ranking list. The proposed goodness measure intuitively captures both (a) the relevance between each individual node in the ranking list and the query; and (b) the diversity among different nodes in the ranking list. Moreover, we propose a scalable algorithm (linear wrt the size of the graph) that generates a provably near-optimal solution. The experimental evaluations on real graphs demonstrate its effectiveness and efficiency.
2D-interval predictions for time series
Authors: Luís Torgo, Orlando Ohashi
Venue: KDD
Year: 2011

Research on time series forecasting is mostly focused on point predictions - models are obtained to estimate the expected value of the target variable for a certain point in future. However, for several relevant applications this type of forecasts has limited utility (e.g. costumer wallet value estimation, wind and electricity power production, control of water quality, etc.). For these domains it is frequently more important to be able to forecast a range of plausible future values of the target variable. A typical example is wind power production, where it is of high relevance to predict the future wind variability in order to ensure that supply and demand are balanced. This type of predictions will allow timely actions to be taken in order to cope with the expected values of the target variable on a certain future time horizon. In this paper we study this type of predictions - the prediction of a range of expected values for a future time interval. We describe some possible approaches to this task and propose an alternative procedure that our extensive experiments on both artificial and real world domains show to have clear advantages.
Mining mobility user profiles for car pooling
Authors: Roberto Trasarti, Fabio Pinelli, Mirco Nanni, Fosca Giannotti
Venue: KDD
Year: 2011

In this paper we introduce a methodology for extracting mobility profiles of individuals from raw digital traces (in particular, GPS traces), and study criteria to match individuals based on profiles. We instantiate the profile matching problem to a specific application context, namely proactive car pooling services, and therefore develop a matching criterion that satisfies various basic constraints obtained from the background knowledge of the application domain. In order to evaluate the impact and robustness of the methods introduced, two experiments are reported, which were performed on a massive dataset containing GPS traces of private cars: (i) the impact of the car pooling application based on profile matching is measured, in terms of percentage shareable traffic; (ii) the approach is adapted to coarser-grained mobility data sources that are nowadays commonly available from telecom operators. In addition the ensuing loss in precision and coverage of profile matches is measured.
Applications of data mining and machine learning in online customer care
Authors: Ravi Vijayaraghavan, P. V. Kannan
Venue: KDD
Year: 2011

With the coming of age of web as a mainstream customer service channel, B2C companies have invested substantial resources in enhancing their web presence. Today customers can interact with a company through channels such as phone, chat, email, social media or web self-service. With the availability of web logs, CRM data and text transcripts these online channels are rich with data and they track several aspects of customer behavior and intent. 24/7 Customer Innovation Labs has developed a series of data mining and statistics driven solutions to improve customer experience in each of these online channels. This talk will focus on solutions to enhance performance of web chat as a customer service channel. 2 stages of customer life-cycle will be considered -- new customer acquisition (or sales) and service of existing customers. In customer acquisition the key objective is to maximize "incremental" revenues via chat. While in customer service the objective is to drive up the quality of customer experience (measured by customer satisfaction surveys or mined customer sentiments) through chat. The solution based on machine learning methods involves:
CHIRP: a new classifier based on composite hypercubes on iterated random projections
Authors: Leland Wilkinson, Anushka Anand, Dang Tuan Nhon
Venue: KDD
Year: 2011

We introduce a classifier based on the L-infinity norm. This classifier, called CHIRP, is an iterative sequence of three stages (projecting, binning, and covering) that are designed to deal with the curse of dimensionality, computational complexity, and nonlinear separability. CHIRP is not a hybrid or modification of existing classifiers; it employs a new covering algorithm. The accuracy of CHIRP on widely-used benchmark datasets exceeds the accuracy of competitors. Its computational complexity is sub-linear in number of instances and number of variables and subquadratic in number of classes.
Combining file content and file relations for cloud based malware detection
Authors: Yanfang Ye, Tao Li, Shenghuo Zhu, Weiwei Zhuang, Egemen Tas, Umesh Gupta, Melih Abdulhayoglu
Venue: KDD
Year: 2011

Due to their damages to Internet security, malware (such as virus, worms, trojans, spyware, backdoors, and rootkits) detection has caught the attention not only of anti-malware industry but also of researchers for decades. Resting on the analysis of file contents extracted from the file samples, like Application Programming Interface (API) calls, instruction sequences, and binary strings, data mining methods such as Naive Bayes and Support Vector Machines have been used for malware detection. However, besides file contents, relations among file samples, such as a "Downloader" is always associated with many Trojans, can provide invaluable information about the properties of file samples. In this paper, we study how file relations can be used to improve malware detection results and develop a file verdict system (named "Valkyrie") building on a semi-parametric classifier model to combine file content and file relations together for malware detection. To the best of our knowledge, this is the first work of using both file content and file relations for malware detection. A comprehensive experimental study on a large collection of PE files obtained from the clients of anti-malware products of Comodo Security Solutions Incorporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our Valkyrie system outperform other popular anti-malware software tools such as Kaspersky AntiVirus and McAfee VirusScan, as well as other alternative data mining based detection systems.
An improved GLMNET for l1-regularized logistic regression
Authors: Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin
Venue: KDD
Year: 2011

GLMNET proposed by Friedman et al. is an algorithm for generalized linear models with elastic net. It has been widely applied to solve L1-regularized logistic regression. However, recent experiments indicated that the existing GLMNET implementation may not be stable for large-scale problems. In this paper, we propose an improved GLMNET to address some theoretical and implementation issues. In particular, as a Newton-type method, GLMNET achieves fast local convergence, but may fail to quickly obtain a useful solution. By a careful design to adjust the effort for each iteration, our method is efficient regardless of loosely or strictly solving the optimization problem. Experiments demonstrate that the improved GLMNET is more efficient than a state-of-the-art coordinate descent method.
Driving with knowledge from the physical world
Authors: Jing Yuan, Yu Zheng, Xing Xie, Guangzhong Sun
Venue: KDD
Year: 2011

This paper presents a Cloud-based system computing customized and practically fast driving routes for an end user using (historical and real-time) traffic conditions and driver behavior. In this system, GPS-equipped taxicabs are employed as mobile sensors constantly probing the traffic rhythm of a city and taxi drivers' intelligence in choosing driving directions in the physical world. Meanwhile, a Cloud aggregates and mines the information from these taxis and other sources from the Internet, like Web maps and weather forecast. The Cloud builds a model incorporating day of the week, time of day, weather conditions, and individual driving strategies (both of the taxi drivers and of the end user for whom the route is being computed). Using this model, our system predicts the traffic conditions of a future time (when the computed route is actually driven) and performs a self-adaptive driving direction service for a particular user. This service gradually learns a user's driving behavior from the user's GPS logs and customizes the fastest route for the user with the help of the Cloud. We evaluate our service using a real-world dataset generated by over 33,000 taxis over a period of 3 months in Beijing. As a result, our service accurately estimates the travel time of a route for a user; hence finding the fastest route customized for the user.
Multi-view transfer learning with a large margin approach
Authors: Dan Zhang, Jingrui He, Yan Liu, Luo Si, Richard D. Lawrence
Venue: KDD
Year: 2011

Transfer learning has been proposed to address the problem of scarcity of labeled data in the target domain by leveraging the data from the source domain. In many real world applications, data is often represented from different perspectives, which correspond to multiple views. For example, a web page can be described by its contents and its associated links. However, most existing transfer learning methods fail to capture the multi-view {nature}, and might not be best suited for such applications.