---
title: "Reinforcement Learning for Recommendations and Search"
url: https://daily.dev/posts/reinforcement-learning-for-recommendations-and-search-hseefp3fk
source_url: https://eugeneyan.com/writing/reinforcement-learning-for-recsys-and-search
type: article
source: "Eugene Yan"
published: 2026-05-31T07:40:37.429Z
updated: 2026-05-31T08:19:02.709Z
tags: ["deep-learning", "reinforcement-learning", "recommendation-systems"]
reading_time: 12
upvotes: 0
comments: 0
language: en
---

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# Reinforcement Learning for Recommendations and Search

**[Eugene Yan](https://daily.dev/sources/eugeneyan)** · 12 min read · 0 upvotes · 0 comments

## Summary

A deep dive into reinforcement learning approaches for recommendation systems and search, covering multi-armed bandits, contextual bandits, value-based methods (DQN), and policy-based methods (REINFORCE). Real-world examples from Yahoo (news article recommendations), Netflix (personalized movie artwork), JD (e-commerce with negative feedback), Microsoft (news with long-term user activeness), ByteDance (joint recommendations and ads), and Google/YouTube (REINFORCE with Boltzmann exploration) are analyzed. Key themes include optimizing for long-term rewards, balancing exploration vs. exploitation, handling cold-start, and off-policy evaluation challenges.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://eugeneyan.com/writing/reinforcement-learning-for-recsys-and-search>

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---

Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning), [#recommendation-systems](https://daily.dev/tags/recommendation-systems)

[View this post on daily.dev](https://daily.dev/posts/reinforcement-learning-for-recommendations-and-search-hseefp3fk)
