# Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)

arXiv: https://arxiv.org/abs/2408.10394 · HTML: https://mlsanity.com/paper/2408.10394/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 19 Aug 2024.

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

- [Joint Modeling and Optimization of Search and Recommendation](https://mlsanity.com/paper/1807.05631/) (2018)
- [Recommendations and Results Organization in Netflix Search](https://mlsanity.com/paper/2105.14134/) (2021)
- [Augmenting Netflix Search with In-Session Adapted Recommendations](https://mlsanity.com/paper/2206.02254/) (2022)
- [Challenges in Search on Streaming Services: Netflix Case Study](https://mlsanity.com/paper/1903.04638/) (2019)

## Built on by (most cited first, 1 of 1)

- [FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications](https://mlsanity.com/paper/2511.14865/) (2025)

## Abstract (arXiv)

Search and recommendation systems are essential in many services, and they are often developed separately, leading to complex maintenance and technical debt. In this paper, we present a unified deep learning model that efficiently handles key aspects of both tasks.