Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark.
Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification.
After reading this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications.
What You Will Learn
Build a spectrum of supervised and unsupervised machine learning algorithmsImplement machine learning algorithms with Spark MLlib librariesDevelop a recommender system with Spark MLlib librariesHandle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model
Who This Book Is For
Data science and machine learning professionals.
Автор: Jos? J. Pazos Arias; Ana Fern?ndez Vilas; Rebeca P Название: Recommender Systems for the Social Web ISBN: 3642446272 ISBN-13(EAN): 9783642446276 Издательство: Springer Рейтинг: Цена: 113180.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces opportunities and challenges that arise in the recommenders` area with the advent of Web 2.0. It presents the mains aspects in the Web 2.0 hype which have to be incorporated in traditional recommender systems.
Автор: Cai-Nicolas Ziegler Название: Social Web Artifacts for Boosting Recommenders ISBN: 331900526X ISBN-13(EAN): 9783319005263 Издательство: Springer Рейтинг: Цена: 130610.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents approaches for exploiting the rapidly expanding fountain of Social Web knowledge by means of classification taxonomies and trust networks, which are used to improve the performance of product-focused recommender systems.
Автор: Yusuf Arslan Название: Improving Marketing Strategies for Private Label Products ISBN: 1799802582 ISBN-13(EAN): 9781799802587 Издательство: Mare Nostrum (Eurospan) Цена: 146910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With changing economic and social environmental conditions and diversified consumer attitudes, national and international competition has increased among retailers. Private label brands have started to follow a dynamic structure in order to adapt themselves to developing environmental conditions. Today, private label products are often mentioned as a mechanism for reaching differentiation in the market and for helping retailers to strengthen consumer loyalty. Improving Marketing Strategies for Private Label Products is a collection of innovative research that examines how some markets are successful and what other markets can do to increase their market share in terms of private label products. It supports in the development of marketing strategies that can help make a private label product more successful. While highlighting topics including e-commerce, national branding, and consumer behavior, this book is ideally designed for marketing professionals, managers, executives, entrepreneurs, business owners, business practitioners, researchers, academicians, and students.
Автор: Francesco Ricci; Lior Rokach; Bracha Shapira Название: Recommender Systems Handbook ISBN: 1489977805 ISBN-13(EAN): 9781489977809 Издательство: Springer Рейтинг: Цена: 172350.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Recommender Systems: Introduction and Challenges.- A Comprehensive Survey of Neighborhood-based Recommendation Methods.- Advances in Collaborative Filtering.- Semantics-aware Content-based Recommender Systems.- Constraint-based Recommender Systems.- Context-Aware Recommender Systems.- Data Mining Methods for Recommender Systems.- Evaluating Recommender Systems.- Evaluating Recommender Systems with User Experiments.- Explaining Recommendations: Design and Evaluation.- Recommender Systems in Industry: A Netflix Case Study.- Panorama of Recommender Systems to Support Learning.- Music Recommender Systems.- The Anatomy of Mobile Location-Based Recommender Systems.- Social Recommender Systems.- People-to-People Reciprocal Recommenders.- Collaboration, Reputation and Recommender Systems in Social Web Search.- Human Decision Making and Recommender Systems.- Privacy Aspects of Recommender Systems.- Source Factors in Recommender System Credibility Evaluation.- Personality and Recommender Systems.- Group Recommender Systems: Aggregation, Satisfaction and Group Attributes.- Aggregation Functions for Recommender Systems.- Active Learning in Recommender Systems.- Multi-Criteria Recommender Systems.- Novelty and Diversity in Recommender Systems.- Cross-domain Recommender Systems.- Robust Collaborative Recommendation.
Автор: Manouselis Nikos Название: Recommender Systems for Technology Enhanced Learning ISBN: 1493905295 ISBN-13(EAN): 9781493905294 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Recommender Systems for Technology Enhanced Learning
Автор: Ricci, Francesco, Rokach, Lior, Shapira, Bracha Название: Recommender Systems Handbook ISBN: 1489976361 ISBN-13(EAN): 9781489976369 Издательство: Springer Рейтинг: Цена: 232910.00 T Наличие на складе: Невозможна поставка. Описание: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems' major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems.
This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Автор: Panagiotis Symeonidis; Andreas Zioupos Название: Matrix and Tensor Factorization Techniques for Recommender Systems ISBN: 3319413562 ISBN-13(EAN): 9783319413563 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a detailed theoretical mathematical background of matrix/tensor factorization techniques and a step-by-step analysis of each method on the basis of an integrated toy example that runs throughout all its chapters and helps the reader to understand the key differences among methods.
Автор: Nikos Manouselis; Hendrik Drachsler; Katrien Verbe Название: Recommender Systems for Technology Enhanced Learning ISBN: 1493946560 ISBN-13(EAN): 9781493946563 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Collaborative Filtering Recommendation of Educational Content in Social Environments utilizing Sentiment Analysis Techniques.- Towards automated evaluation of learning resources inside repositories.- Linked Data and the Social Web as facilitators for TEL recommender systems in research and practice.- The Learning Registry: Applying Social Metadata for Learning Resource Recommendations.- A Framework for Personalised Learning-Plan Recommendations in Game-Based Learning.- An approach for an Affective Educational Recommendation Model.- The Case for Preference-Inconsistent Recommendations.- Further Thoughts on Context-Aware Paper Recommendations for Education.- Towards a Social Trust-aware Recommender for Teachers.- ALEF: from Application to Platform for Adaptive Collaborative Learning.- Two Recommending Strategies to enhance Online Presence in Personal Learning Environments.- Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem.- COCOON CORE: CO-Author Recommendations based on Betweenness Centrality and Interest Similarity.- Scientific Recommendations to Enhance Scholarly Awareness and Foster Collaboration.
Автор: Fatih Gedikli Название: Recommender Systems and the Social Web ISBN: 3658019476 ISBN-13(EAN): 9783658019471 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: ГЇВїВЅ There is an increasing demand for recommender systems due to the information overload users are facing on the Web. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources.
Автор: Patricia Victor; Chris Cornelis; Martine De Cock Название: Trust Networks for Recommender Systems ISBN: 9491216392 ISBN-13(EAN): 9789491216398 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Featuring innovative contributions to the field such as a new bilattice-based model for trust and distrust, this book on a hot research topic is the first in-depth study of the potential of distrust in the emerging domain of trust-enhanced recommendation.
Автор: Charu C. Aggarwal Название: Recommender Systems ISBN: 3319296574 ISBN-13(EAN): 9783319296579 Издательство: Springer Рейтинг: Цена: 62410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.
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