Search Results for "statistical-methods-for-recommender-systems"

Statistical Methods for Recommender Systems

Statistical Methods for Recommender Systems

  • Author: Deepak K. Agarwal,Bee-Chung Chen
  • Publisher: Cambridge University Press
  • ISBN: 1316565130
  • Category: Computers
  • Page: N.A
  • View: 845
DOWNLOAD NOW »
Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

Recommender Systems for Information Providers

Recommender Systems for Information Providers

Designing Customer Centric Paths to Information

  • Author: Andreas W. Neumann
  • Publisher: Springer Science & Business Media
  • ISBN: 3790821349
  • Category: Business & Economics
  • Page: 158
  • View: 494
DOWNLOAD NOW »
Information providers are a very promising application area of recommender systems due to the general problem of assessing the quality of information products prior to the purchase. Recommender systems automatically generate product recommendations: customers profit from a faster finding of relevant products, stores profit from rising sales. All aspects of recommender systems are covered: the economic background, mechanism design, a survey of systems in the Internet, statistical methods and algorithms, service oriented architectures, user interfaces, as well as experiences and data from real-world applications. Specific solutions for areas with strong privacy concerns, scalability issues for large collections of products, as well as algorithms to lessen the cold-start problem for a faster return on investment of recommender projects are addressed. This book describes all steps it takes to design, implement, and successfully operate a recommender system for a specific information platform.

Modern Statistical Methods for HCI

Modern Statistical Methods for HCI

  • Author: Judy Robertson,Maurits Kaptein
  • Publisher: Springer
  • ISBN: 3319266330
  • Category: Computers
  • Page: 348
  • View: 464
DOWNLOAD NOW »
This book critically reflects on current statistical methods used in Human-Computer Interaction (HCI) and introduces a number of novel methods to the reader. Covering many techniques and approaches for exploratory data analysis including effect and power calculations, experimental design, event history analysis, non-parametric testing and Bayesian inference; the research contained in this book discusses how to communicate statistical results fairly, as well as presenting a general set of recommendations for authors and reviewers to improve the quality of statistical analysis in HCI. Each chapter presents [R] code for running analyses on HCI examples and explains how the results can be interpreted. Modern Statistical Methods for HCI is aimed at researchers and graduate students who have some knowledge of “traditional” null hypothesis significance testing, but who wish to improve their practice by using techniques which have recently emerged from statistics and related fields. This book critically evaluates current practices within the field and supports a less rigid, procedural view of statistics in favour of fair statistical communication.

Recommender Systems for Technology Enhanced Learning

Recommender Systems for Technology Enhanced Learning

Research Trends and Applications

  • Author: Nikos Manouselis,Hendrik Drachsler,Katrien Verbert,Olga C. Santos
  • Publisher: Springer Science & Business Media
  • ISBN: 1493905309
  • Category: Computers
  • Page: 306
  • View: 6378
DOWNLOAD NOW »
As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years. Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices. Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.

Recommender Systems Handbook

Recommender Systems Handbook

  • Author: Francesco Ricci,Lior Rokach,Bracha Shapira,Paul B. Kantor
  • Publisher: Springer Science & Business Media
  • ISBN: 9780387858203
  • Category: Computers
  • Page: 842
  • View: 8492
DOWNLOAD NOW »
The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

Social Web Artifacts for Boosting Recommenders

Social Web Artifacts for Boosting Recommenders

Theory and Implementation

  • Author: Cai-Nicolas Ziegler
  • Publisher: Springer
  • ISBN: 3319005278
  • Category: Computers
  • Page: 187
  • View: 5453
DOWNLOAD NOW »
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the “Web 2.0” or the “Social Web”: Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties – when used as proxies for interest similarity – are able to mitigate the recommenders' scalability problem.

Predicting movie ratings and recommender systems

Predicting movie ratings and recommender systems

A 195-page monograph on machine learning, recommender systems, and the Netflix Prize.

  • Author: Arkadiusz Paterek
  • Publisher: Arkadiusz Paterek
  • ISBN: N.A
  • Category: Mathematics
  • Page: 196
  • View: 3465
DOWNLOAD NOW »
A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems. What's inside:introduction to predictive modeling,a comprehensive summary of the Netflix Prize, the most known machine learning competition, with a $1M prize,detailed description of a top-50 Netflix Prize solution predicting movie ratings,summary of the most important methods published - RMSE's from different papers listed and grouped in one place,detailed analysis of matrix factorizations / regularized SVD,how to interpret the factorization results - new, most informative movie genres,how to adapt the algorithms developed for the Netflix Prize to calculate good quality personalized recommendations,dealing with the cold-start: simple content-based augmentation,description of two rating-based recommender systems,commentary on everything: novel and unique insights, know-how from over 9 years of practicing and analysing predictive modeling.

Statistical Methods in Computer Security

Statistical Methods in Computer Security

  • Author: William W.S. Chen
  • Publisher: CRC Press
  • ISBN: 1420030884
  • Category: Mathematics
  • Page: 376
  • View: 6033
DOWNLOAD NOW »
Statistical Methods in Computer Security summarizes discussions held at the recent Joint Statistical Meeting to provide a clear layout of current applications in the field. This blue-ribbon reference discusses the most influential advancements in computer security policy, firewalls, and security issues related to passwords. It addresses crime and misconduct on the Internet, considers the development of infrastructures that may prevent breaches of security and law, and illustrates the vulnerability of networked computers to new virus attacks despite widespread deployment of antivirus software, firewalls, and other network security equipment.

Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods

Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods

Contextual Advancements and New Methods

  • Author: Dehuri, Satchidananda
  • Publisher: IGI Global
  • ISBN: 1466625430
  • Category: Computers
  • Page: 350
  • View: 1568
DOWNLOAD NOW »
Although recommendation systems have become a vital research area in the fields of cognitive science, approximation theory, information retrieval and management sciences, they still require improvements to make recommendation methods more effective and intelligent. Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods is a comprehensive collection of research on the latest advancements of intelligence techniques and their application to recommendation systems and how this could improve this field of study.