A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems

TL;DR

This survey reviews retrieval algorithms used in ad and content recommendation systems, highlighting their differences, effectiveness, and the methods that optimize personalization and user experience.

Contribution

It provides a comprehensive comparison of retrieval techniques in ad and content recommendation systems, emphasizing their unique requirements and effective algorithms.

Findings

Personalized ad targeting relies on detailed user profiles.

Content recommendation focuses on matching user preferences.

Effective retrieval methods vary between ad and content systems.

Abstract

This survey examines the most effective retrieval algorithms utilized in ad recommendation and content recommendation systems. Ad targeting algorithms rely on detailed user profiles and behavioral data to deliver personalized advertisements, thereby driving revenue through targeted placements. Conversely, organic retrieval systems aim to improve user experience by recommending content that matches user preferences. This paper compares these two applications and explains the most effective methods employed in each.