The impact of YouTube recommendation system on video views

By Renjie Zhou, Samamon Khemmarat, Lixin Gao. ACM/SIGCOMM Internet Measurement Conference, 2010

384 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Identifying the sentiment styles of YouTube’s vloggers (2018, background): “Furthermore, related video recommendations shown during or after a playing video also serve as an important indicator of the view count for YouTube videos (Zhou et al., 2010).”
Viral Video Style: A Closer Look at Viral Videos on YouTube (2014, background): “It also includes the key events, locations and demographics about the audience [20].”
Science communication on YouTube: Factors that affect channel and video popularity (2016, background): “That is, the recommendation system recommends popular videos to viewers, which in turn increases the popularity of those videos (Zhou et al., 2010).”
Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems (2015, background): “It has been shown that the position of a video in a related video list plays a critical role in the click-through rate (Zhou et al., 2010).”
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, methodology): “Literature Adversarial Setting Target model Poisoning Technique Application Backdoor Year Knowledge Capability [138] L-K IN [159], contextual, personalization DI personalized web services − 2013 [57] P-K MO Deep Learning DM image classification − 2017 [119] L-K MO Deep Learning, SVM, random forest D”