Personalizing LinkedIn Feed

41 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.

Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, background): “Several web-scale production systems have been described in the past [1, 7, 21].”
Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit (2019, background): “Lots of research efforts have been made for this task by considering user data such as rating [5], contents [2, 17, 32], and reviews [39].”
GraphJet: Real-Time Content Recommendations at Twitter (2016, background): “Although there is a tremendous amount of work on recommender systems in the academic literature, relatively few papers describe actual production systems, and those that do (e.g., [29, 10, 1]) focus mostly on algorithms, as opposed to systems infrastructure—which is the perspective we take.”
Making Contextual Decisions with Low Technical Debt (2016, background): “ies (and hence do not “close the loop" in Figure 1). Contextual bandits deployments. There have been several applications of contextual bandit learning in web services (e.g., news recommendation [4, 33, 34] and advertising [12]). However, they have all been one-offs rather than a generalpurpose”