Case Studies & Lessons Learned
Case studies in recommender systems provide valuable insights into the practical application of theoretical models and algorithms, allowing researchers and practitioners to understand how these systems perform in real-world settings. By examining specific implementations, such as those used by Amazon, Netflix, or Spotify, we can identify the strengths, limitations, and challenges associated with different recommendation techniques. This helps to bridge the gap between academic research and industry practice, offering lessons on scalability, user interaction, and performance optimization. Additionally, case studies highlight the contextual factors influencing system success, such as user behavior, domain-specific constraints, and business objectives.
Some case studies about recommender systems are as follows (these are posts from our Blog):
Netflix’s Journey: Early Challenges and the Absence of Recommender Systems in Marc Randolph’s Vision
A recent 2-hour interview with Marc Randolph, the co-founder and first CEO of Netflix, provides a fascinating look into the early days of the company. Unlike many discussions about Netflix, this interview does not focus on the recommender systems that have become synonymous with the company’s success. This absence is intriguing given that Netflix often […]
Case Study on how VistaPrint uses Amazon Personalize for product recommendations
Nothing mind-blowing, but a nice read if you have some time: A blog post by VistaPrint on how they use Amazon Personalize for creating personalized product recommendations. Here is a summary of the blog post: VistaPrint, part of Cimpress, serves millions of small businesses worldwide, offering marketing products and solutions for over two decades. The […]
A look inside TikTok’s all-knowing algorithm [Charlotte Hu]
The New York Times recently reported about leaked documents on how Tik Tok’s recommender system works (PayWall). The report followed an investigation of the Wall Street Journal on the same topic (also PayWall). Charlotte Hu from “Popular Science” summarized the reports. More: https://www.popsci.com/technology/tiktok-algorithm/.
Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them [Beel & Dinesh]
The article “Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching Them” provides an in-depth look into the practical and technical challenges faced when developing recommender systems tailored for academia. (Long version of arXiv; short version on CEUR-WS). Over a period of six years, the authors built and operated three […]
On YouTube’s recommendation system [Cristos Goodrow]
Cristos Goodrow, VP of Engineering at YouTube, published a blog post about YouTube’s recommender system that provides “a deeper look into how YouTube’s recommendation system works”. It includes a general introduction on what recommender systems are, how they are used on YouTube, and an overview of what information YouTube uses for the recommender system. Cristos […]
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 […]
‘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, […]
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 […]
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 […]
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 […]