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Recommendation Engines
How companies like Amazon and Netflix know what “you might also like”: the history, technology, business, and social 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.”Schrage offers a history of recommendation that reaches back to antiquity's oracles and astrologers; recounts the academic origins and commercial evolution of recommendation engines; explains how these systems work, discussing key mathematical insights, including the impact of machine learning and deep learning algorithms; and highlights user experience design challenges.He offers brief but incisive case studies of the digital music service Spotify; ByteDance, the owner of TikTok; and the online personal stylist Stitch Fix.Finally, Schrage considers the future of technological recommenders: Will they leave us disappointed and dependent—or will they help us discover the world and ourselves in novel and serendipitous ways?
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A Gentle Noble's Vacation Recommendation, Volume 10
When Lizel mysteriously finds himself in a city that bears odd similarities to his own but clearly isn't, he quickly comes to terms with the unlikely truth: this is an entirely different world.Even so, laid-back Lizel isn't the type to panic. He immediately sets out to learn more about this strange place, and to help him do so, hires a seasoned adventurer named Gil as his tour guide and protector. Until he's able to find a way home, Lizel figures this is a perfect opportunity to explore a new way of life adventuring as part of a guild.After all, he's sure he'll go home eventually... might as well enjoy the otherworldly vacation for now!
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Computing Taste : Algorithms and the Makers of Music Recommendation
Meet the people who design the algorithms that capture our musical tastes. The people who make music recommender systems have lofty goals: they want to broaden listeners’ horizons and help obscure musicians find audiences, taking advantage of the enormous catalogs offered by companies like Spotify, Apple Music, and Pandora.But for their critics, recommender systems seem to embody all the potential harms of algorithms: they flatten culture into numbers, they normalize ever-broadening data collection, and they profile their users for commercial ends.Drawing on years of ethnographic fieldwork, anthropologist Nick Seaver describes how the makers of music recommendation navigate these tensions: how product managers understand their relationship with the users they want to help and to capture; how scientists conceive of listening itself as a kind of data processing; and how engineers imagine the geography of the world of music as a space they care for and control. Computing Taste rehumanizes the algorithmic systems that shape our world, drawing attention to the people who build and maintain them.In this vividly theorized book, Seaver brings the thinking of programmers into conversation with the discipline of anthropology, opening up the cultural world of computation in a wide-ranging exploration that travels from cosmology to calculation, myth to machine learning, and captivation to care.
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Writing Recommendation Letters : The Discourse of Evaluation in Academic Settings
Even though reading and writing recommendation letters is one of the essential service tasks of the professorial life of academics, there are few resources to train graduate students and junior academics on how to draft a successful recommendation letter for different academic purposes.Writing Recommendation Letters draws linguistic and rhetorical principles from close to a thousand real-world examples of academic letters of recommendation.As a result, the research that informs the pedagogy is extensive, current, and highly relevant to the discourse of evaluation in academic settings with findings that have implications for genre-based writing instruction, English for Academic Purposes (EAP), and teaching of academic literacies.The authors are two experienced college professors who regularly teach graduate students and mentor young academics, so the reflection questions and instructor suggestions they provide were designed for today’s university classroom.The result is an instructive book that translates academic discourse structures and principles into accessible language, supplying authentic examples and ample writing practice. Key FeaturesReaders will learn the theoretical context that defines the genre of letters of recommendation.The book highlights the similarities and differences between the three different types of letters of recommendation: letters written for graduate admission, letters written in support of fellowship applications, and letters written to support obtaining a faculty position.Chapters on different aspects of linguistic and rhetorical features discuss presenting the applicants' credentials, highlighting the strengths of their character, accentuating and downplaying certain traits, as well as the pros and cons of boilerplate language and the use of customary frames for opening and closing. Readers will see real-world examples of actual letters of recommendation to see how seasoned faculty build the case for the applicant.
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Paddington Sightseeing Adventure
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AmScope Adapter Combination - Canon Camera to AmScope RMS objective Macro Photography Recommendation
AmScope Adapter Combination - Canon Camera to AmScope RMS objective Macro Photography Recommendation
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Exploration and Discovery
The discovery and mapping of the world, its peoples, oceans and continents, are the result of centuries of exploration.This book traces the history of such travel and the pioneers who pushed back the frontiers of the known world.It explains the reasons for their journeys, whether out of scientific curiosity, to create trading opportunities, to spread religious beliefs or for personal gain or glory.With the aid of over 250 illustrations, maps and photographs, this accessible reference work captures all the excitement and spirit of adventure.
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Building Recommendation Systems in Python and Jax : Hands-On Production Systems at Scale
Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available.Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way. In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale.You'll learn the math, ideas, and implementation details you need to succeed.This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, Weights & Biases, and Kafka. You'll learn:The data essential for building a RecSysHow to frame your data and business as a RecSys problemWays to evaluate models appropriate for your systemMethods to implement, train, test, and deploy the model you chooseMetrics you need to track to ensure your system is working as plannedHow to improve your system as you learn more about your users, products, and business case
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