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Methodologies for Improving Modern Industrial Recommender Systems
July 21, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Shusen Wang
arXiv ID
2308.01204
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
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.
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