mlsanityData to 14 Oct 2026

Creator-Side Recommender System: Challenges, Designs, and Applications

arXiv 2502.20497 ↗First posted 25 Feb 2025cs.IR cs.LGcited by 0

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Abstract (arXiv)

Users and creators are two crucial components of recommender systems. Typical recommender systems focus on the user side, providing the most suitable items based on each user's request. In such scenarios, a few items receive a majority of exposures, while many items receive very few. This imbalance leads to poorer experiences and decreased activity among the creators receiving less feedback, harming the recommender system in the long term. To this end, we develop a creator-side recommender system, called DualRec, to answer the following question: how to find the most suitable users for each item to enhance the creators' experience? We show that typical user-side recommendation algorithms, such as retrieval and ranking algorithms, can be adapted into the creator-side versions with just a few modifications. This greatly simplifies algorithm design in DualRec. Moreover, we discuss a unique challenge in DualRec: the user availability issue, which is not present in user-side recommender systems. To tackle this issue, we incorporate a user availability calculation (UAC) module to effectively enhance DualRec's performance. DualRec has already been implemented in Kwai, a short video recommendation system with over 100 millions user and over 10 million creators, significantly improving the experience for creators.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 25 Feb 2025.

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17 of 27 references.

References · 17

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2017Neural Collaborative Filteringcited by 1,111
  3. 2017Deep Interest Network for Click-Through Rate Predictioncited by 544
  4. 2020Embedding-based Retrieval in Facebook Searchcited by 156
  5. 2023Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisitedcited by 124
  6. 2018Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabacited by 105
  7. 2021Sequential Recommendation with Graph Neural Networkscited by 89
  8. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 78
  9. 2017Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Timecited by 47
  10. 2020A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendationcited by 38
  11. 2023When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendationcited by 17
  12. 2024Personalized Audiobook Recommendations at Spotify Through Graph Neural Networkscited by 14
5 more
  1. 2022Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)cited by 12
  2. 2024UniSAR: Modeling User Transition Behaviors between Search and Recommendationcited by 12
  3. 2017Efficient Large-scale Approximate Nearest Neighbor Search on the GPUcited by 8
  4. 2023Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendationcited by 7
  5. 2024Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendationcited by 3

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