# Recommendations and Results Organization in Netflix Search

arXiv: https://arxiv.org/abs/2105.14134 · HTML: https://mlsanity.com/paper/2105.14134/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2021.

## Built on by (most cited first, 4 of 4)

- [Beyond NDCG: behavioral testing of recommender systems with RecList](https://mlsanity.com/paper/2111.09963/) (2021)
- [Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity](https://mlsanity.com/paper/2308.00177/) (2023)
- [Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)](https://mlsanity.com/paper/2408.10394/) (2024)
- [Can you recommend content to creatives instead of final consumers? A RecSys based on user's preferred visual styles](https://mlsanity.com/paper/2208.10902/) (2022)

## Abstract (arXiv)

Personalized recommendations on the Netflix Homepage are based on a user's viewing habits and the behavior of similar users. These recommendations, organized for efficient browsing, enable users to discover the next great video to watch and enjoy without additional input or an explicit expression of their intents or goals. The Netflix Search experience, on the other hand, allows users to take active control of discovering new videos by explicitly expressing their entertainment needs via search queries. In this talk, we discuss the importance of producing search results that go beyond traditional keyword-matches to effectively satisfy users' search needs in the Netflix entertainment setting. Motivated by users' various search intents, we highlight the necessity to improve Search by applying approaches that have historically powered the Homepage. Specifically, we discuss our approach to leverage recommendations in the context of Search and to effectively organize search results to provide a product experience that meaningfully adds value for our users.