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Methodologies for Improving Modern Industrial Recommender Systems

arXiv 2308.01204 ↗First posted 21 Jul 2023cs.IR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 21 Jul 2023.

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13 of 22 references.

References · 13

  1. 2016Wide & Deep Learning for Recommender Systemscited by 747
  2. 2017Deep Interest Network for Click-Through Rate Predictioncited by 544
  3. 2020DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systemscited by 327
  4. 2020Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Predictioncited by 177
  5. 2018Learning Tree-based Deep Model for Recommender Systemscited by 99
  6. 2019FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Predictioncited by 83
  7. 2023PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Informationcited by 60
  8. 2016Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptationcited by 40
  9. 2021Sparse-Interest Network for Sequential Recommendationcited by 38
  10. 2020COLD: Towards the Next Generation of Pre-Ranking Systemcited by 31
  11. 2020Large Scale Product Graph Construction for Recommendation in E-commercecited by 28
  12. 2020M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systemscited by 21
1 more
  1. 2021Path-based Deep Network for Candidate Item Matching in Recommenderscited by 3

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