WSDM'19


Book Description

We are delighted to welcome you to the proceedings volume of the Twelfth ACM International Conference onWeb Search and Data Mining (WSDM 2019), held in Melbourne, Australia on February 11-15, 2019. Now in its twelfth year, WSDMis a top tier conference in web-inspired research relating to search and data mining. Headline features of WSDM 2019 will include the sequence of very strong keynote presentations by Jaime Teevan, H. V. Jagadish, Rohit Prasad, Aleksandra Korolova, and Maarten de Rijke, and we thank them for their willingness to attend the conference and provide their insights. As in previous years, there was a strong pool of research submissions, with 511 full-length papers received, a similar number compared to Los Angeles (2018) and Cambridge (2017), which had 514 and 505 submissions respectively. The submissions originated from 43 countries. Of the papers received, 84 were accepted and are included in these proceedings (the same number as were accepted in 2018), representing an acceptance rate of 16.4% (versus 16.1% in 2018) and within the range established through the previous eleven years (minimum 15.5%, maximum 22.3%). The final 84 papers are from 23 countries and span six continents, making WSDM a truly international forum. In 2019 we continued the single-track model that is a hallmark of WSDM, with a mix of long-talk presentations and short-talk presentations, an approach that was introduced in 2012. Of the 84 accepted papers, 50 were assigned a five-minute talk, and 34 were assigned a longer fifteen-minute slot. All authors also had an opportunity to engage in detailed interactions during a 45-minute afternoon poster session held on the same day as their oral presentation. The selection between long-talk and short-talk presentation was made by the Program Chairs, based on whether the topic and content of the paper was better suited to large group presentation or to focused and interactive presentation, and was independent of the paper reviewing process. All accepted papers have equal standing in the proceedings. The program committee worked hard to assess the submissions and provide feedback to help improve the papers. At least three program committee members reviewed each submission, together with a senior PC member who oversaw discussion amongst the reviewers and provided an overall recommendation. To discuss papers and ensure consistency across the entire decision process a PC meeting of available SPC members and the PC Chairs was held in Los Angeles on October 18th, with other SPC members participating remotely. We acknowledge the tremendous work of the 208 members of the Program Committee (representing 27 countries), and in particular the efforts of the 65 Senior Program Committee members. They are the cornerstone of the high-quality program that has emerged in 2019. After discussion with past WSDM chairs and the WSDM Steering Committee, the reviewing model used in 2019 was a mix of single- and double-blind evaluation. Submissions were prepared using a doubleblind format, with no author information allowed (however authors were permitted to indicate the source of their data set and/or deployment environment, to avoid references to major commercial search engines as has occurred in the past). First-tier PC members had no access to author names or affiliations, allowing them to provide double-blind bias-free review. However, to more rigorously vet for conflict of interest (CoI) issues between authors and first-tier referees, SPC members could see both author names and author affiliations.




Recommender Systems Handbook


Book Description

This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool.




Grid Computing: The New Frontier of High Performance Computing


Book Description

The book deals with the most recent technology of distributed computing.As Internet continues to grow and provide practical connectivity between users of computers it has become possible to consider use of computing resources which are far apart and connected by Wide Area Networks.Instead of using only local computing power it has become practical to access computing resources widely distributed. In some cases between different countries in other cases between different continents.This idea of using computer power is similar to the well known electric power utility technology. Hence the name of this distributed computing technology is the Grid Computing.Initially grid computing was used by technologically advanced scientific users.They used grid computing to experiment with large scale problems which required high performance computing facilities and collaborative work.In the next stage of development the grid computing technology has become effective and economically attractive for large and medium size commercial companies.It is expected that eventually the grid computing style of providing computing power will become universal reaching every user in industry and business. * Written by academic and industrial experts who have developed or used grid computing* Many proposed solutions have been tested in real life applications* Covers most essential and technically relevant issues in grid computing




Geometry and Statistics


Book Description

Geometry and Statistics, Volume 46 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Geometry and Statistics







Question Answering over Text and Knowledge Base


Book Description

This book provides a coherent and complete overview of various Question Answering (QA) systems. It covers three main categories based on the source of the data that can be unstructured text (TextQA), structured knowledge graphs (KBQA), and the combination of both. Developing a QA system usually requires using a combination of various important techniques, including natural language processing, information retrieval and extraction, knowledge graph processing, and machine learning. After a general introduction and an overview of the book in Chapter 1, the history of QA systems and the architecture of different QA approaches are explained in Chapter 2. It starts with early close domain QA systems and reviews different generations of QA up to state-of-the-art hybrid models. Next, Chapter 3 is devoted to explaining the datasets and the metrics used for evaluating TextQA and KBQA. Chapter 4 introduces the neural and deep learning models used in QA systems. This chapter includes the required knowledge of deep learning and neural text representation models for comprehending the QA models over text and QA models over knowledge base explained in Chapters 5 and 6, respectively. In some of the KBQA models the textual data is also used as another source besides the knowledge base; these hybrid models are studied in Chapter 7. In Chapter 8, a detailed explanation of some well-known real applications of the QA systems is provided. Eventually, open issues and future work on QA are discussed in Chapter 9. This book delivers a comprehensive overview on QA over text, QA over knowledge base, and hybrid QA systems which can be used by researchers starting in this field. It will help its readers to follow the state-of-the-art research in the area by providing essential and basic knowledge.




Data Science with Semantic Technologies


Book Description

Gone are the days when data was interlinked with related data by humans and human interpretation was required. Data is no longer just data. It is now considered a Thing or Entity or Concept with meaning, so that a machine not only understands the concept but also extrapolates the way humans do. Data Science with Semantic Technologies: Deployment and Exploration, the second volume of a two-volume handbook set, provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book offers the answer to various questions like: What makes a technology semantic as opposed to other approaches to data science? What is knowledge data science? How does knowledge data science relate to other fields? This book explores the optimal use of these technologies to provide the highest benefit to the user under one comprehensive source and title. As there is no dedicated book available in the market on this topic at this time, this book becomes a unique resource for scholars, researchers, data scientists, professionals, and practitioners. This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation.







Multi-armed Bandit Problem and Application


Book Description

In recent years, the multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance. This success is due to its stellar performance combined with attractive properties, such as learning from less feedback. The multiarmed bandit field is currently experiencing a renaissance, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This book aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize the state-of-the-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.




Artificial Intelligence for Human Computer Interaction: A Modern Approach


Book Description

This edited book explores the many interesting questions that lie at the intersection between AI and HCI. It covers a comprehensive set of perspectives, methods and projects that present the challenges and opportunities that modern AI methods bring to HCI researchers and practitioners. The chapters take a clear departure from traditional HCI methods and leverage data-driven and deep learning methods to tackle HCI problems that were previously challenging or impossible to address. It starts with addressing classic HCI topics, including human behaviour modeling and input, and then dedicates a section to data and tools, two technical pillars of modern AI methods. These chapters exemplify how state-of-the-art deep learning methods infuse new directions and allow researchers to tackle long standing and newly emerging HCI problems alike. Artificial Intelligence for Human Computer Interaction: A Modern Approach concludes with a section on Specific Domains which covers a set of emerging HCI areas where modern AI methods start to show real impact, such as personalized medical, design, and UI automation.