# Augmenting Netflix Search with In-Session Adapted Recommendations

arXiv: https://arxiv.org/abs/2206.02254 · HTML: https://mlsanity.com/paper/2206.02254/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 5 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation](https://mlsanity.com/paper/2007.12000/) (2020)
- [Exploring Longitudinal Effects of Session-based Recommendations](https://mlsanity.com/paper/2008.07226/) (2020)
- [Challenges in Search on Streaming Services: Netflix Case Study](https://mlsanity.com/paper/1903.04638/) (2019)

## Built on by (most cited first, 3 of 3)

- [LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models](https://mlsanity.com/paper/2404.08847/) (2024)
- [IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning](https://mlsanity.com/paper/2408.05353/) (2024)
- [Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)](https://mlsanity.com/paper/2408.10394/) (2024)

## 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.