Creator-Side Recommender System: Challenges, Designs, and Applications
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916634927562752 |
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| author | Chen, Xiaoshuang Wang, Yibo Wang, Yao Liu, Husheng Zhan, Kaiqiao Wang, Ben Gai, Kun |
| author_facet | Chen, Xiaoshuang Wang, Yibo Wang, Yao Liu, Husheng Zhan, Kaiqiao Wang, Ben Gai, Kun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Creator-Side Recommender System: Challenges, Designs, and Applications Chen, Xiaoshuang Wang, Yibo Wang, Yao Liu, Husheng Zhan, Kaiqiao Wang, Ben Gai, Kun Information Retrieval Machine Learning 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. |
| title | Creator-Side Recommender System: Challenges, Designs, and Applications |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2502.20497 |