OpenRaiser/Intern-Atlas huggingface dataset:
Scaling up Link Prediction with Ensembles
Authors: Liang Duan, Charu C. Aggarwal, Shuai Ma, Renjun Hu, Jinpeng Huai
Venue: WSDM
Year: 2016

A network with $n$ nodes contains O(n2) possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Furthermore, even though link prediction is closely related to missing value estimation problems, such as collaborative filtering, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity over very large networks. Due to this computational complexity, most known link prediction methods are designed for evaluating the link propensity over a specified subset of links, rather than for performing a global search over the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this paper, we propose an ensemble enabled approach to scaling up link prediction, which is able to decompose traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with the use of latent factor models, which can be effectively implemented over networks of modest size. Furthermore, the ensemble enabled approach has several advantages in terms of performance. We show the advantage of using ensemble-based latent factor models with experiments on very large networks. Experimental results demonstrate the effectiveness and scalability of our approach.
Temporal Formation and Evolution of Online Communities
Authors: Hossein Fani
Venue: WSDM
Year: 2016

Researchers have already studied the identification of online communities and the possible impact or influence relationships from several perspectives. For instance, communities of users that are formed based on shared relationships and topological similarities, or communities that consist of users that share similar content. However, little work has been done on detection of communities that simultaneously share topical and temporal similarities. Furthermore, these studies have not explored the causation relationship between the communities. Causation provides systematic explanation as to why communities are formed and helps to predict future communities. This proposal will address two main research questions: i) how can communities that share topical and temporal similarities be identified, and ii) how can causation relation between different online communities be detected and modelled. We model users' behaviour towards topics of interest through multivariate time series to identify like-minded communities. Further, we employ Granger's concept of causality to infer causation between detected communities from corresponding users' time series. Granger causality is the prominent approach in time series modelling and rests on a firm statistical foundation. We assess the proposed community detection methods through comparison with the state of the art and verify the causal model through its prediction accuracy.
TargetAd2016: 2nd International Workshop on Ad Targeting at Scale
Authors: Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic
Venue: WSDM
Year: 2016

The 2nd International Workshop on Ad Targeting at Scale will be held in San Francisco, California, USA on February 22nd, 2016, colocated with the 9th ACM International Conference on Web Search and Data Mining (WSDM). The main objective of the workshop is to address the challenges of ad targeting in web-scale settings. The workshop brings together interdisciplinary researchers in computational advertising, recommender systems, personalization, and related areas, to share, exchange, learn, and develop preliminary results, new concepts, ideas, principles, and methodologies on applying data mining technologies to ad targeting. We have constructed an exciting program of eight refereed papers and several invited talks that will help us better understand the future of ad targeting.
Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search & Analytics
Authors: Dhruv Gupta
Venue: WSDM
Year: 2016

In this article, I present the questions that I seek to answer in my PhD research. I posit to analyze natural language text with the help of semantic annotations and mine important events for navigating large text corpora. Semantic annotations such as named entities, geographic locations, and temporal expressions can help us mine events from the given corpora. These events thus provide us with useful means to discover the locked knowledge in them. I pose three problems that can help unlock this knowledge vault in semantically annotated text corpora: i. identifying important events; ii. semantic search; iii. and event analytics.
Understanding Diffusion Processes: Inference and Theory
Authors: Xinran He
Venue: WSDM
Year: 2016

With increasing popularity of social media and social networks sites, analyzing the social networks offers great potential to shed light on human social structure and provides great marketing opportunities. Usually, social network analysis starts with extracting or learning the social network and the associated parameters. Contrary to other analytical tasks, this step is highly non-trivial due to amorphous nature of social ties and the challenges of noisy and incomplete observations. My research focuses on improving accuracy in inferring the network as well as analyzing the consequences when the extracted network is noisy or erroneous. To be more precise, I propose to study the following two questions with a special focus on analyzing diffusion behaviors: (1) How to utilize special properties of social networks to improve accuracy of the extracted network under noisy and missing data; (2) How to characterize the impact of noise in the inferred network and carry out robust analysis and optimization.
How Relevant is the Irrelevant Data: Leveraging the Tagging Data for a Learning-to-Rank Model
Authors: Noor Ifada, Richi Nayak
Venue: WSDM
Year: 2016

For the task of tag-based item recommendations, the underlying tensor model faces several challenges such as high data sparsity and inferring latent factors effectively. To overcome the inherent sparsity issue of tensor models, we propose the graded-relevance interpretation scheme that leverages the tagging data effectively. Unlike the existing schemes, the graded-relevance scheme interprets the tagging data richly, differentiates the non-observed tagging data insightfully, and annotates each entry as one of the "relevant", "likely relevant", "irrelevant", or "indecisive" labels. To infer the latent factors of tensor models correctly to produce the high quality recommendation, we develop a novel learning-to-rank method, Go-Rank, that optimizes Graded Average Precision (GAP). Evaluating the proposed method on real-world datasets, we show that the proposed interpretation scheme produces a denser tensor model by revealing "relevant" entries from the previously assumed "irrelevant" entries. Optimizing GAP as the ranking metric, the quality of the recommendations generated by Go-Rank is found superior against the benchmarking methods.
Web-scale Multimedia Search for Internet Video Content
Authors: Lu Jiang
Venue: WSDM
Year: 2016

The Internet has been witnessing an explosion of video content. According to a Cisco study, video content is estimated to account for 80% of all the entire world's internet traffic by 2019. Video data are becoming one of the most valuable sources to assess information and knowledge. However, existing video search solutions are still based on text matching (text-to-text search), and could fail for the huge volumes of videos that have little relevant metadata or no metadata at all. The need for large-scale and intelligent video search, which bridges the gap between the user's information need and the video content, seems to be urgent.
Reducing Click and Skip Errors in Search Result Ranking
Authors: Jiepu Jiang, James Allan
Venue: WSDM
Year: 2016

Search engines provide result summaries to help users quickly identify whether or not it is worthwhile to click on a result and read in detail. However, users may visit non-relevant results and/or skip relevant ones. These actions are usually harmful to the user experience, but few considered this problem in search result ranking. This paper optimizes relevance of results and user click and skip activities at the same time. Comparing two equally relevant results, our approach learns to rank the one that users are more likely to click on at a higher position. Similarly, it demotes non-relevant web pages with high click probabilities. Experimental results show this approach reduces about 10%-20% of the click and skip errors with a trade off of 2.1% decline in [email protected]
Quality Management in Crowdsourcing using Gold Judges Behavior
Authors: Gabriella Kazai, Imed Zitouni
Venue: WSDM
Year: 2016

Crowdsourcing relevance labels has become an accepted practice for the evaluation of IR systems, where the task of constructing a test collection is distributed over large populations of unknown users with widely varied skills and motivations. Typical methods to check and ensure the quality of the crowd's output is to inject work tasks with known answers (gold tasks) on which workers' performance can be measured. However, gold tasks are expensive to create and have limited application. A more recent trend is to monitor the workers' interactions during a task and estimate their work quality based on their behavior. In this paper, we show that without gold behavior signals that reflect trusted interaction patterns, classifiers can perform poorly, especially for complex tasks, which can lead to high quality crowd workers getting blocked while poorly performing workers remain undetected. Through a series of crowdsourcing experiments, we compare the behaviors of trained professional judges and crowd workers and then use the trained judges' behavior signals as gold behavior to train a classifier to detect poorly performing crowd workers. Our experiments show that classification accuracy almost doubles in some tasks with the use of gold behavior data.
Extracting Search Query Patterns via the Pairwise Coupled Topic Model
Authors: Takuya Konishi, Takuya Ohwa, Sumio Fujita, Kazushi Ikeda, Kohei Hayashi
Venue: WSDM
Year: 2016

A fundamental yet new challenge in information retrieval is the identification of patterns behind search queries. For example, the query "NY restaurant" and "boston hotel" shares the common pattern "LOCATION SERVICE". However, because of the diversity of real queries, existing approaches require data preprocessing by humans or specifying the target query domains, which hinders their applicability.
Understanding Offline Political Systems by Mining Online Political Data
Authors: David Lazer, Oren Tsur, Tina Eliassi-Rad
Venue: WSDM
Year: 2016

"Man is by nature a political animal", as asserted by Aristotle. This political nature manifests itself in the data we produce and the traces we leave online. In this tutorial, we address a number of fundamental issues regarding mining of political data: What types of data could be considered political? What can we learn from such data? Can we use the data for prediction of political changes, etc? How can these prediction tasks be done efficiently? Can we use online socio-political data in order to get a better understanding of our political systems and of recent political changes? What are the pitfalls and inherent shortcomings of using online data for political analysis? In recent years, with the abundance of data, these questions, among others, have gained importance, especially in light of the global political turmoil and the upcoming 2016 US presidential election. We introduce relevant political science theory, describe the challenges within the framework of computational social science and present state of the art approaches bridging social network analysis, graph mining, and natural language processing.
Crowdsourcing High Quality Labels with a Tight Budget
Authors: Qi Li, Fenglong Ma, Jing Gao, Lu Su, Christopher J. Quinn
Venue: WSDM
Year: 2016

In the past decade, commercial crowdsourcing platforms have revolutionized the ways of classifying and annotating data, especially for large datasets. Obtaining labels for a single instance can be inexpensive, but for large datasets, it is important to allocate budgets wisely. With limited budgets, requesters must trade-off between the quantity of labeled instances and the quality of the final results. Existing budget allocation methods can achieve good quantity but cannot guarantee high quality of individual instances under a tight budget. However, in some scenarios, requesters may be willing to label fewer instances but of higher quality. Moreover, they may have different requirements on quality for different tasks. To address these challenges, we propose a flexible budget allocation framework called Requallo. Requallo allows requesters to set their specific requirements on the labeling quality and maximizes the number of labeled instances that achieve the quality requirement under a tight budget. The budget allocation problem is modeled as a Markov decision process and a sequential labeling policy is produced. The proposed policy greedily searches for the instance to query next as the one that can provide the maximum reward for the goal. The Requallo framework is further extended to consider worker reliability so that the budget can be better allocated. Experiments on two real-world crowdsourcing tasks as well as a simulated task demonstrate that when the budget is tight, the proposed Requallo framework outperforms existing state-of-the-art budget allocation methods from both quantity and quality aspects.
Is Mail The Next Frontier In Search And Data Mining?
Authors: Yoelle Maarek
Venue: WSDM
Year: 2016

The nature of Web mail traffic has significantly evolved in the last two decades, and consequently the behavior of Web mail users has also changed. For instance a recent study conducted by Yahoo Labs showed that today 90% of Web mail traffic is machine-generated. This partly explains why email traffic continues to grow even if a significant amount of personal communications has moved towards social media. Most users today are receiving in their inbox important invoices, receipts, and travel itineraries, together with non-malicious junk mail such as hotel newsletters or shopping promotions that could safely ignore. This is one of the reasons that a majority of messages remain unread, and many are deleted without being read. In that sense, Web mail has become quite similar to traditional snail mail. In spite of this drastic change in nature, many mail features remain unchanged. While 70% of mail users do not define even a single folder, folders are still predominant in the left trail of many Web mail clients. Mail search results are still mostly ranked by date, which makes the retrieving of older messages extremely challenging. This is even more painful to users, as unlike in Web search, they will know when a relevant previously read message has not been returned.
Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources
Authors: Ahmad Mahmoody, Matteo Riondato, Eli Upfal
Venue: WSDM
Year: 2016

Detecting new information and events in a dynamic network by probing individual nodes has many practical applications: discovering new webpages, analyzing influence properties in network, and detecting failure propagation in electronic circuits or infections in public drinkable water systems. In practice, it is infeasible for anyone but the owner of the network (if existent) to monitor all nodes at all times. In this work we study the constrained setting when the observer can only probe a small set of nodes at each time step to check whether new pieces of information (items) have reached those nodes. We formally define the problem through an infinite time generating process that places new items in subsets of nodes according to an unknown probability distribution. Items have an exponentially decaying novelty, modeling their decreasing value. The observer uses a probing schedule (i.e., a probability distribution over the set of nodes) to choose, at each time step, a small set of nodes to check for new items. The goal is to compute a schedule that minimizes the average novelty of undetected items. We present an algorithm, WIGGINS, to compute the optimal schedule through convex optimization, and then show how it can be adapted when the parameters of the problem must be learned or change over time. We also present a scalable variant of WIGGINS for the MapReduce framework. The results of our experimental evaluation on real social networks demonstrate the practicality of our approach.
Barbara Made the News: Mining the Behavior of Crowds for Time-Aware Learning to Rank
Authors: Flávio Martins, João Magalhães, Jamie Callan
Venue: WSDM
Year: 2016

In Twitter, and other microblogging services, the generation of new content by the crowd is often biased towards immediacy: what is happening now. Prompted by the propagation of commentary and information through multiple mediums, users on the Web interact with and produce new posts about newsworthy topics and give rise to trending topics. This paper proposes to leverage on the behavioral dynamics of users to estimate the most relevant time periods for a topic. Our hypothesis stems from the fact that when a real-world event occurs it usually has peak times on the Web: a higher volume of tweets, new visits and edits to related Wikipedia articles, and news published about the event. In this paper, we propose a novel time-aware ranking model that leverages on multiple sources of crowd signals. Our approach builds on two major novelties. First, a unifying approach that given query q, mines and represents temporal evidence from multiple sources of crowd signals. This allows us to predict the temporal relevance of documents for query q. Second, a principled retrieval model that integrates temporal signals in a learning to rank framework, to rank results according to the predicted temporal relevance. Evaluation on the TREC 2013 and 2014 Microblog track datasets demonstrates that the proposed model achieves a relative improvement of 13.2% over lexical retrieval models and 6.2% over a learning to rank baseline.
Second Workshop on Search and Exploration of X-Rated Information (SEXI'16): WSDM Workshop Summary
Authors: Vanessa Murdock, Charles L. A. Clarke, Jaap Kamps, Jussi Karlgren
Venue: WSDM
Year: 2016

Adult content is pervasive on the web, has been a driving factor in the adoption of the Internet medium, and is responsible for a significant fraction of traffic and revenues, yet rarely attracts attention in research. The research questions surrounding adult content access behaviors are unique, and interesting and valuable research in this area can be done ethically. WSDM 2016 features a half day workshop on Search and Exploration of X-Rated Information (SEXI) for information access tasks related to adult content. While the scope of the workshop remains broad, special attention is devoted to the privacy and security issues surrounding adult content by inviting keynote speakers with extensive experience on these topics. The recent release of the personal data belonging to customers of the adult dating site Ashley Madison provides a timely context for the focus on privacy and security.
The Past and Future of Systems for Current Events
Authors: Mor Naaman
Venue: WSDM
Year: 2016

People share in social media an overwhelming amount of content from real-world events. These events range from major global events like an uprising or an earthquake, to local events and emergencies such as a fire or a parade; from media events like the Oscar's, to events that enjoy little media coverage such as a conference or a music concert. This shared media represents an important part of our society, culture and history. At the same time, this social media content is still fragmented across services, hard to find, and difficult to consume and understand.
Semantic Documents Relatedness using Concept Graph Representation
Authors: Yuan Ni, Qiongkai Xu, Feng Cao, Yosi Mass, Dafna Sheinwald, Huijia Zhu, Shao Sheng Cao
Venue: WSDM
Year: 2016

We deal with the problem of document representation for the task of measuring semantic relatedness between documents. A document is represented as a compact concept graph where nodes represent concepts extracted from the document through references to entities in a knowledge base such as DBpedia. Edges represent the semantic and structural relationships among the concepts. Several methods are presented to measure the strength of those relationships. Concepts are weighted through the concept graph using closeness centrality measure which reflects their relevance to the aspects of the document. A novel similarity measure between two concept graphs is presented. The similarity measure first represents concepts as continuous vectors by means of neural networks. Second, the continuous vectors are used to accumulate pairwise similarity between pairs of concepts while considering their assigned weights. We evaluate our method on a standard benchmark for document similarity. Our method outperforms state-of-the-art methods including ESA (Explicit Semantic Annotation) while our concept graphs are much smaller than the concept vectors generated by ESA. Moreover, we show that by combining our concept graph with ESA, we obtain an even further improvement.
The Predictive Power of Massive Data about our Fine-Grained Behavior
Authors: Foster J. Provost
Venue: WSDM
Year: 2016

What really is it about "big data" that makes it different from traditional data? In this talk I illustrate one important aspect: massive ultra-fine-grained data on individuals' behaviors holds remarkable predictive power. I examine several applications to marketing-related tasks, showing how machine learning methods can extract the predictive power and how the value of the data "asset" seems different from the value of traditional data used for predictive modeling.
Mining the Web for Intelligent Problem Solving for Programmers
Authors: Xin Rong
Venue: WSDM
Year: 2016

Programming can be hard to learn and master. Novice programmers often find themselves struggling with terminology, concepts, or different solutions to the same problem with little clue on how to choose the best one. Professional programmers often spend a considerable amount of time learning to use third-party libraries, APIs, or an unfamiliar piece of code. Although programmers can turn to search engines or question-and-answer websites for help, the problem solving process can often take multiple iterations and can be time-consuming. An integrated system that can recognize a programmer's difficulties and provide contextualized solutions is thus desirable, as it may significantly reduce the amount of manual effort required in the loop of troubleshooting.