A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems
It provides a comparative overview of existing methods for two key recommendation domains, but is incremental as it surveys rather than introduces new techniques.
This survey compares retrieval algorithms for ad recommendation systems, which use user profiles and behavioral data for targeted advertising to drive revenue, and content recommendation systems, which aim to improve user experience by matching user preferences.
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.