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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 2
After their first successful adventure together, Lizel has officially formed a party with his guard and companion, the famous adventurer Gil.A renowned swordsman known by the moniker Single Stroke for his ability to take down any enemy with just one swipe, Gil has promised to protect Lizel as they become an official part of the adventurer's guild — and the two are already making waves!Lizel's charming personality has earned him a few friends by now, among them a young appraiser named Judge, the grandson of a rich merchant from the mercantile capital of Marcade.When he asks them to accompany him as guards on a trip to visit his grandfather, Lizel happily accepts the opportunity to sightsee and explore in such a famous country.Gil, on the other hand, is more excited about seeing if there are any super-strong monsters to fight in Marcade's Labyrinth.It's time for the newly-formed party to prove their mettle!
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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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What PC recommendation is there for video editing?
For video editing, it is recommended to have a PC with a powerful multi-core processor (such as an Intel Core i7 or AMD Ryzen 7), at least 16GB of RAM, a dedicated graphics card with at least 4GB of VRAM, and a fast solid-state drive (SSD) for storage. Additionally, having a high-resolution display and a good cooling system is important for handling the demands of video editing software. It's also important to consider the specific requirements of the video editing software you plan to use, as some may have specific hardware recommendations.
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What is your recommendation for a video to MP3 converter?
I recommend using a reliable and reputable video to MP3 converter such as 4K Video Downloader or Any Video Converter. These tools are user-friendly and offer a variety of features, including the ability to convert videos from various sources such as YouTube, Vimeo, and other popular platforms. Additionally, they provide high-quality MP3 output and support batch conversion, making it convenient for users to convert multiple videos at once. It's important to choose a converter that is safe, free of malware, and offers regular updates to ensure a smooth and secure conversion process.
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Search for a recommendation.
I recommend using a combination of online search engines and review websites to find recommendations for the product or service you are looking for. Look for reputable sources such as consumer reports, industry experts, and customer reviews to get a well-rounded view of the options available. Additionally, consider reaching out to friends, family, and colleagues for their personal recommendations based on their experiences. By gathering information from multiple sources, you can make a more informed decision.
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What is your recommendation?
My recommendation would be to carefully consider all the options available to you and weigh the pros and cons of each. It's important to prioritize your needs and goals when making a decision. Additionally, seeking advice from trusted sources or professionals in the field can provide valuable insights to help you make an informed choice. Ultimately, trust your instincts and choose the option that aligns best with your values and objectives.
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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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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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RoyJones Anatomical Orthopedic Heel Spur Recommendation Gel Sole Daily Women's Slippers 1002 38 black
RoyJones Anatomical Orthopedic Heel Spur Recommendation Gel Sole Daily Women's Slippers JEL03 ; Plush wool insole is used for comfortable use. The soft polyurethane gel in the upper layer chamber where the foot presses works in perfect harmony with Roy Jones' superior foot anatomy. It is suitable for domestic use. A wear-resistant outsole with multi-directional grip is used. The same product as in the image and all our products are sent to your name with an invoice. Our products are produced in Turkey. Domestic Capital is domestic production. Product Patterns are Full Pattern, Take the number you wear. For example; If your foot is size 37, get size 37. Gender: Female footLength 36 23 cm 37 23.5 cm 38 24 cm 39 24.5 cm 40 25 cm 41 25.5 cm usage: Daily Upper Material: Faux Leather: Polyurethane Ingredient: Faux Leather Heel Height: 4.5 CmListed By ENTEGRA
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RoyJones Anatomical Orthopedic Heel Spur Recommendation Gel Sole Daily Women's Slippers 1001 38 black
RoyJones Anatomical Orthopedic Heel Spur Recommendation Gel Sole Daily Women's Slippers JEL02 ; Plush wool insole is used for comfortable use. The soft polyurethane gel in the upper layer chamber where the foot presses works in perfect harmony with Roy Jones' superior foot anatomy. It is suitable for domestic use. A wear-resistant outsole with multi-directional grip is used. The same product as in the image and all our products are sent to your name with an invoice. Our products are produced in Turkey. Domestic Capital is domestic production. Product Patterns are Full Pattern, Take the number you wear. For example; If your foot is size 37, get size 37. Gender: Female 36 23cm 37 23.5cm 38 24cm 39 24.5cm 40 25cm 41 25.5cm usage: Daily Upper Material: Faux Leather: Polyurethane Ingredient: Faux Leather Heel Height: 4.5 CmListed By ENTEGRA
Price: 19.29 £ | Shipping*: 0.0 £
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What is recommendation 795?
Recommendation 795 is a guideline issued by the International Labour Organization (ILO) concerning the protection of workers' personal data. It emphasizes the importance of safeguarding workers' privacy and ensuring that their personal data is collected and processed in a fair and transparent manner. The recommendation also calls for measures to be put in place to prevent the misuse of personal data and to provide workers with the necessary information and tools to exercise their data protection rights. Overall, Recommendation 795 aims to promote a safe and secure working environment where workers' personal data is respected and protected.
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What is Recommendation 798?
Recommendation 798 is a set of guidelines developed by the International Labour Organization (ILO) to promote the development and implementation of employment policies that support full, productive, and freely chosen employment for all. It emphasizes the importance of creating an enabling environment for job creation, promoting equal opportunities and treatment in employment, and ensuring social protection for all workers. Recommendation 798 also encourages member states to work towards eliminating discrimination in employment and to promote the rights of workers to organize and bargain collectively.
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How can you change the video format in the video editing program Lightworks?
In Lightworks, you can change the video format by using the export feature. First, import the video into the program and make any necessary edits. Then, go to the export tab and select the desired video format from the list of available options. You can also customize the resolution, frame rate, and other settings before exporting the video in the new format. Finally, save the changes and export the video in the new format.
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What is video encoding and video decoding?
Video encoding is the process of converting a video file from one format to another, typically to reduce its file size or to make it compatible with a specific device or platform. This process involves compressing the video data using various algorithms to maintain visual quality while reducing the file size. Video decoding, on the other hand, is the process of converting a compressed video file back to its original format so that it can be viewed or played on a device. Decoding involves decompressing the video data and reconstructing the original video frames for playback. Both encoding and decoding are essential processes for video streaming, storage, and playback.
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