mlsanityData to 7 Oct 2026

Augmenting Netflix Search with In-Session Adapted Recommendations

arXiv 2206.02254 ↗First posted 5 Jun 2022cs.IR cs.AI cs.LGcited by 3

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Abstract (arXiv)

We motivate the need for recommendation systems that can cater to the members in-the-moment intent by leveraging their interactions from the current session. We provide an overview of an end-to-end in-session adaptive recommendations system in the context of Netflix Search. We discuss the challenges and potential solutions when developing such a system at production scale.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 5 Jun 2022.

Builds on

4 of 10 references.

References · 4

  1. 2018Self-Attentive Sequential Recommendationcited by 1,169
  2. 2020ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendationcited by 22
  3. 2020Exploring Longitudinal Effects of Session-based Recommendationscited by 9
  4. 2019Challenges in Search on Streaming Services: Netflix Case Studycited by 2

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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Modelscited by 1
  2. 2024IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learningcited by 1
  3. 2024Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)cited by 1