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Author: Mattias Frey
Publisher: Univ of California Press
ISBN: 0520382048
Category : BUSINESS & ECONOMICS
Languages : en
Pages : 282
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Book Description
Introduction -- Why we need film and series suggestions -- How algorithmic recommender systems work -- Cracking the code, part I : developing Netflix's recommendation algorithms -- Cracking the code, part II : unpacking Netflix's myth of big data -- How real people choose films and series -- Afterword : robot critics vs. human experts -- Appendix : designing the empirical audience study.
Author: Mattias Frey
Publisher: Univ of California Press
ISBN: 0520382048
Category : BUSINESS & ECONOMICS
Languages : en
Pages : 282
Get Book Here
Book Description
Introduction -- Why we need film and series suggestions -- How algorithmic recommender systems work -- Cracking the code, part I : developing Netflix's recommendation algorithms -- Cracking the code, part II : unpacking Netflix's myth of big data -- How real people choose films and series -- Afterword : robot critics vs. human experts -- Appendix : designing the empirical audience study.
Author: Mattias Frey
Publisher: Univ of California Press
ISBN: 0520382021
Category : Social Science
Languages : en
Pages : 282
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Book Description
Algorithmic recommender systems, deployed by media companies to suggest content based on users’ viewing histories, have inspired hopes for personalized, curated media but also dire warnings of filter bubbles and media homogeneity. Curiously, both proponents and detractors assume that recommender systems for choosing films and series are novel, effective, and widely used. Scrutinizing the world’s most subscribed streaming service, Netflix, this book challenges that consensus. Investigating real-life users, marketing rhetoric, technical processes, business models, and historical antecedents, Mattias Frey demonstrates that these choice aids are neither as revolutionary nor as alarming as their celebrants and critics maintain—and neither as trusted nor as widely used. Netflix Recommends brings to light the constellations of sources that real viewers use to choose films and series in the digital age and argues that although some lament AI’s hostile takeover of humanistic cultures, the thirst for filters, curators, and critics is stronger than ever.
Author: Rounak Banik
Publisher: Packt Publishing Ltd
ISBN: 1788992539
Category : Computers
Languages : en
Pages : 141
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Book Description
With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.
Author: Efraim Turban
Publisher: Springer
ISBN: 3319170287
Category : Business & Economics
Languages : en
Pages : 331
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Book Description
This is a multidisciplinary textbook on social commerce by leading authors of e-commerce and e-marketing textbooks, with contributions by several industry experts. It is effectively the first true textbook on this topic and can be used in one of the following ways: Textbook for a standalone elective course at the undergraduate or graduate levels (including MBA and executive MBA programs) Supplementary text in marketing, management or Information Systems disciplines Training courses in industry Support resources for researchers and practitioners in the fields of marketing, management and information management The book examines the latest trends in e-commerce, including social businesses, social networking, social collaboration, innovations and mobility. Individual chapters cover tools and platforms for social commerce; supporting theories and concepts; marketing communications; customer engagement and metrics; social shopping; social customer service and CRM contents; the social enterprise; innovative applications; strategy and performance management; and implementing social commerce systems. Each chapter also includes a real-world example as an opening case; application cases and examples; exhibits; a chapter summary; review questions and end-of-chapter exercises. The book also includes a glossary and key terms, as well as supplementary materials that include PowerPoint lecture notes, an Instructor’s Manual, a test bank and five online tutorials.
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 177
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Book Description
Unlock the Power of Big Data Analytics in the Modern World Are you ready to dive into the fascinating world of big data analytics? "Big Data for Beginners" is your essential guide to understanding and harnessing the potential of big data in the modern era. Whether you're new to the concept or looking to expand your knowledge, this comprehensive book equips you with the foundational knowledge and tools to navigate the complexities of big data and make informed decisions. Key Features: 1. Introduction to Big Data: Dive deep into the fundamental concepts of big data, from its definition to its significance in today's data-driven landscape. Build a strong foundation that empowers you to navigate the vast world of big data. 2. Understanding Data Sources: Navigate the diverse sources of big data, including structured, semi-structured, and unstructured data. Learn how to gather, process, and manage data from various sources to extract valuable insights. 3. Big Data Technologies: Discover the technologies that power big data analytics. Explore tools like Hadoop, Spark, and NoSQL databases, understanding their role in processing and analyzing massive datasets. 4. Data Storage and Processing: Master the art of storing and processing big data effectively. Learn about distributed file systems, data warehouses, and batch and real-time processing to ensure scalability and efficiency. 5. Data Analysis and Visualization: Uncover strategies for analyzing and visualizing big data. Explore techniques for data exploration, pattern recognition, and creating compelling visual representations that convey insights effectively. 6. Machine Learning and Predictive Analytics: Delve into the world of machine learning and predictive analytics using big data. Learn how to build models that make accurate predictions and informed decisions based on massive datasets. 7. Big Data Security and Privacy: Explore the challenges of securing and preserving privacy in the realm of big data. Learn how to implement encryption, access controls, and anonymization techniques to protect sensitive information. 8. Real-World Applications: Discover the myriad applications of big data across industries. From healthcare to finance, retail to marketing, explore how big data is transforming business operations and decision-making. 9. Challenges and Future Trends: Gain insights into the challenges posed by big data, such as data quality and scalability issues. Explore the future trends and advancements that are shaping the evolution of big data analytics. 10. Ethical Considerations: Delve into the ethical considerations surrounding big data. Learn about responsible data usage, addressing bias, and maintaining transparency in the collection and analysis of data. Who This Book Is For: "Big Data for Beginners" is an indispensable resource for individuals, students, professionals, and enthusiasts who are eager to grasp the fundamentals of big data analytics. Whether you're a beginner curious about the world of data or an experienced professional seeking to enhance your skills, this book will guide you through the intricacies and empower you to harness the potential of big data.
Author: John R. Schermerhorn, Jr
Publisher: John Wiley & Sons
ISBN: 0470169648
Category : Business & Economics
Languages : en
Pages : 557
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Book Description
Exploring Management, Second Edition by John Schermerhorn, presents a new and exciting approach in teaching and learning the principles of management. This text is organized within a unique learning system tailored to students’ reading and study styles. It offers a clean, engaging and innovative approach that motivates students and helps them understand and master management principles.
Author: Kalorth, Nithin
Publisher: IGI Global
ISBN:
Category : Social Science
Languages : en
Pages : 318
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Book Description
The rapid increase in popularity of major streaming services is having a massive impact on more traditional media outlets. Over-the-Top (OTT) Media is the term given to these types of services, which bypass the traditional media sources through an internet connection. How will OTT media force traditional forms of media to adjust and adapt in order to remain relevant? The Rise of Over-the-Top (OTT) Media and Implications for Media Consumption and Production is a timely edited volume that delves into the transformative emergence of Over-the-Top (OTT) media, which is reshaping the landscape of media consumption and production. The book traces the historical roots of OTT media, establishing a contextual understanding of its rapid rise and impact on the industry. Analyzing the complex web of business models and revenue streams in the OTT industry, the publication sheds light on the competitive dynamics, the entry of new players, and the subsequent effects on traditional media companies. It offers a fresh perspective, recognizing OTT media as a distinct and transformative medium, different from conventional film and television studies. Navigating the myriad aspects of OTT media, the book examines market trends and dynamics, showcasing the intricate technological infrastructure of OTT services, encompassing platforms, devices, and delivery methods. Engaging with contemporary issues, the book investigates the intersections of OTT media with news, entertainment, advertising, marketing, and the global south, fostering a holistic understanding of its far-reaching impact. As an essential reference for scholars, researchers, and media professionals, this book not only helps unravel the complexities of this rapidly evolving medium but also equips its readers with valuable insights to navigate the dynamic digital media landscape.
Author: Michael Schrage
Publisher: MIT Press
ISBN: 0262358786
Category : Technology & Engineering
Languages : en
Pages : 306
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Book Description
How companies like Amazon, Netflix, and Spotify know what "you might also like": the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences "you might also like."
Author: Djoymi Baker
Publisher: Taylor & Francis
ISBN: 1000900061
Category : Social Science
Languages : en
Pages : 161
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Book Description
Focusing on Netflix’s child and family-orientated platform exclusive content, this book offers the first exploration of a controversial genre cycle of dark science fiction, horror, and fantasy television under Netflix’s "Family Watch Together TV" tag. Using a ground-breaking mix of methods including audience research, interface, and textual analysis, the book demonstrates how Netflix is producing dark family telefantasy content that is both reshaping child and family-friendly TV genres and challenging earlier broadcast TV models around child-appropriate family viewing. It illuminates how Netflix encourages family audiences to "watch together" through intergenerational dynamics that work on and offscreen. The chapters in this book explore how this "Netflixication" of family television developed across landmark examples including Stranger Things, A Series of Unfortunate Events, The Dark Crystal: Age of Resistance, and even Squid Game. The book outlines how Netflix is consolidating a new dark family terrain in the streaming sector, which is unsettling older concepts of family viewing, leading to considerable audience and critical confusion around target audiences and viewer expectations. This book will be of particular interest to upper-level undergraduates, graduates, and scholars in the fields of television studies, screen genre studies, childhood studies, and cultural studies.
Author: Lisa Parks
Publisher: University of Illinois Press
ISBN: 0252054873
Category : Social Science
Languages : en
Pages : 226
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Book Description
Exploring how we make, distribute, and consume today’s media systems Media backends--the electronics, labor, and operations behind our screens--significantly influence our understanding of the sociotechnical relations, economies, and operations of media. Lisa Parks, Julia Velkova, and Sander De Ridder assemble essays that delve into the evolving politics of the media infrastructural landscape. Throughout, the contributors draw on feminist, queer, and intersectional criticism to engage with infrastructural and industrial issues. This focus reflects a concern about the systemic inequalities that emerge when tech companies and designers fail to address workplace discrimination and algorithmic violence and exclusions. Moving from smart phones to smart dust, the essayists examine topics like artificial intelligence, human-machine communication, and links between digital infrastructures and public service media alongside investigations into the algorithmic backends at Netflix and Spotify, Google’s hyperscale data centers, and video-on-demand services in India. A fascinating foray into an expanding landscape of media studies, Media Backends illuminates the behind-the-screen processes influencing our digital lives. Contributors: Mark Andrejevic, Philippe Bouquillion, Jonathan Cohn, Faithe J. Day, Sander De Ridder, Fatima Gaw, Christine Ithurbide, Anne Kaun, Amanda Lagerkvist, Alexis Logsdon, Stine Lomborg, Tim Markham, Vicki Mayer, Rahul Mukherjee, Kaarina Nikunen, Lisa Parks, Vibodh Parthasarathi, Philipp Seuferling, Ranjit Singh, Jacek Smolicki, Fredrik Stiernstedt, Matilda Tudor, Julia Velkova, and Zala Volcic
