Methodologies for Improving Modern Industrial Recommender Systems

TL;DR

This paper discusses practical methodologies for enhancing modern industrial recommender systems, focusing on real-world applications and improvements in key performance metrics like retention and user engagement.

Contribution

It provides industry-tested strategies and insights for improving RS performance, based on real-world experiences rather than theoretical or publicly documented methods.

Findings

Methodologies tested in real industrial RSs

Improvements in retention and engagement metrics

Practical industry experience shared

Abstract

Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS engineers who are diligently working to improve their key performance indicators, such as retention and duration. The experiences shared in this paper have been tested in some real industrial RSs and are likely to be generalized to other RSs as well. Most contents in this paper are industry experience without publicly available references.