Example Applications

Recommender Systems are used in a variety of applications. The most popular ones being probably movies, news, and e-commerce. However, recommendation systems are used in many applications more. To get an initial idea of what recommender systems are and what they can do, have a look at the following blog posts.

Netflix launches ‘Downloads for You’: Automatic Downloads of Recommended Series and Movies

Three years after Netflix introduced ‘Smart Downloads‘, Patrick Flemming (Director, Product Innovation) announces ‘Downloads For You’. Downloads For You, a new feature that automatically downloads recommended shows or movies to your mobile device based on your tastes. https://about.netflix.com/en/news/downloads-for-you-takes-on-the-go-to-the-next-level As of now, it’s only available on Android apps. iOS will be following soon.

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Google Scholar re-designs its research-paper recommender system

Google Scholar re-designed its recommender system as announced by Namit Shetty, Alex Verstak, Kyu Jin Hwang, Linghua Jin, Philippe David, and Anurag Acharya. Your Scholar Recommendations just got better – fresher, more relevant, and easier to scan. If you have a Scholar profile and are actively publishing, your Scholar homepage should have recommended articles that […]

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Porsche adds a recommendation system to its car configurator

Porsche announced in a press release that Porsche has added a recommendation function to its Car Configurator. Porsche claims to have trained 270 neural networks on several million data points and they report an astonishing 90% accuracy. I am not sure though, how accuracy was calculated exactly. A user can start the recommendation engine by […]

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Google adds personalization features to its Pixel phones

Harrison Lingren from Google announced a number of new features for Google’s Pixel phones, some relating to personalization. These features include Adaptive Sound, which adjusts the sound of your phone based on the surroundings. Adaptive Battery, which “automatically saves even more power if a user is likely to miss their next charge”. It also automatically […]

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Apple introduces Fitness+, a “new engaging and personalized fitness experience”

Apple announced Fitness+, a new app that is supposed to enable personalized fitness workouts and hence competes with Peloton. According to Apple’s press release: Apple Fitness+ intelligently incorporates metrics from Apple Watch for users to visualize right on their iPhone, iPad, or Apple TV, offering a first-of-its-kind personalized workout experience. Everyone from beginners to committed […]

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Ocilion and Xroad Media launch a recsys for TV shows

ocilion – provider of IPTV solutions – and Xroad Media – provider of ‘content discovery solutions’ – partner up. They integrate Xroad Media’s recommender system Ncanto into the TV streaming products of ocilion. The first pilot partner is the Grman Salzburg AG, which offers its customers a ‘Plus‘ service with recommendations for TV shows and […]

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Spotify considers the interests of artists when recommending music to users

Spotify announced a new feature that allows artists and music labels to influence how their music is recommended to the users of Spotify. In this new experiment, artists and labels can identify music that’s a priority for them, and our system will add that signal to the algorithm that determines personalized listening sessions. This allows […]

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arXiv now has a recommender system, provided by CORE

arXiv — the preprint repository — and CORE — no, not the Australian conference and journal ranking but the “world’s largest collection of open access research papers” — announced a partnership. This partnership allows arXiv to provide research-paper recommendations to its users, provided by the CORE recommendation API. arXiv readers now have a faster way to […]

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StrainBrain: A Recommender System for Weed

I just stumbled upon StrainBrain, a Cannabis Strain Recommendation Engine. According to their website, they are supported, among others, by Google Cloud for Start-Ups. The system seems to be simple. You choose a number of characteristics … … … and then you get your recommendations. StrainBrain claims to use deep neural networks for their recommendations. […]

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Mozilla is Crowdsourcing Research into (Harmful) YouTube Recommendations [Ashley Boyd @ Mozilla]

YouTube recommendations can be delightful, but they can also be dangerous. The platform has a history of recommending harmful content — from pandemic conspiracies to political disinformation — to its users, even if they’ve previously viewed harmless content. Indeed, in October 2019, Mozilla published its own research on the topic, revealing that YouTube has recommended harmful videos, […]

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