Methodologies for Improving Modern Industrial Recommender Systems
This provides incremental improvements for experienced recommender system engineers in industrial settings.
The paper tackles the problem of enhancing modern industrial recommender systems by sharing tested industry experiences aimed at improving key performance indicators like retention and duration, with claims of generalizability to other systems.
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.