Case Studies

On YouTube’s recommendation system [Cristos Goodrow]

Cristos Goodrow, VP of Engineering at YouTube, wrote a blog post that provides “a deeper look into how YouTube’s recommendation system works”. The blog post is still a high-level description but yet an informative read. Recommendations drive a significant amount of the overall viewership on YouTube, even more than channel subscriptions or search. […] When […]

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Apple’s opt-in for cross-app advertisement leads to a 15% – 20% drop in revenue, says Brian Bowman

A few months ago, Apple changed its practice regarding user-tracking across apps. Since then, Apple requires app developers to explicitly ask users whether they agree to the tracking. Now, the first results are public about how this affects revenues. VentureBeat reports that, according to Brian Bowman, “Apple’s IDFA change has triggered 15% to 20% revenue […]

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‘Rex’: Medium’s Recommender-System as a Microservice [Miles Hinson]

Miles Hinson writes about his experience in developing ‘Rex’, the recommender system for Medium, as a Microservice. He discusses design choices and presents a few numbers such as that 95% of all recommendations are created within less than 1 second. However, the biggest issue was language choice. Much of Medium runs within a Node.js monolith, […]

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Against All Odds: Netflix, From Mail-Order DVD Rentals to Streaming Dominance [Shawn Knight @ TechSpot]

Shown Knight from Techspot writes about the history of Netflix and how Netflix evolved from a mail-order DVD rental to the world’s largest streaming service. Home video rentals were already a $16 billion industry when Reed Hastings and Marc Randolph decided to get involved in the summer of 1997. Hastings, who holds degrees in mathematics […]

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What Twitter learned from the Recsys 2020 Challenge [Michael Bronstein et al.]

‘What Twitter learned from the Recsys 2020 Challenge’ is a blog post authored by Michael Bronstein, Luca Belli, Apoorv Sharma, Yuanpu Xie, Ying Xiao, Dan Shiebler, Max Hansmire, and Wenzhe Shi; published on TowardsDataScience. The authors describe the RecSys 2020 Challenge that was performed on Twitter data. [We] describe the dataset and the three winning […]

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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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Case study how simple recommendation techniques improve user experience in e-Commerce [Emilio Carrión @ MercadonaTech]

Emilio Carrión (MercadonaTech) wrote a blog post about how simple recommendations to substitute ‘ghost’ products improve conversion rate and user experience. He defines ‘ghost’ products as items that are available at the time a user adds them to the shopping cart, but are not available any more when the user proceeds to check-out. It seems […]

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Facebook documents its recommender system — a little bit. [Sarah Perez @ Techcrunch]

Facebook has not been very open about how its recommender system works exactly. This may be about to change though. Today, Techcrunch announced that Facebook “has, for the first time, made public how its content recommendation guidelines work”. However, Techcrunch also points out that there is little technical detail on how recommendations work. Algorithmic recommendation […]

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The Architecture Used at LinkedIn to Improve Feature Management in Machine Learning Models [Jesus Rodriguez@Medium]

The scale of the machine learning problems that an organization like LinkedIn deals with results incomprehensible for data scientists. Building an maintaining a single, effective machine learning models is hard enough so imagine coordinating the execution of thousands of machine learning programs to achieve a cohesive experience. Feature engineering is one of the key element […]

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How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages? [Dokyun Lee & Kartik Hosanagar]

An interesting study on the impact of recommender systems in e-Commerce. While, on average, views, conversion|views, and final conversion rates varied by 15.3%, 21.6%, and 7.5%, respectively, the numbers strongly depended on the product attributes. The study is based on an A/B test (recommendations vs. no recommendations) with 184,375 users. We find that the lift […]

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