Creator-Side Recommender System: Challenges, Designs, and Applications
Xiaoshuang Chen, Yibo Wang, Yao Wang, Husheng Liu, Kaiqiao Zhan, Ben, Wang, Kun Gai

TL;DR
This paper introduces DualRec, a creator-side recommender system designed to improve creator experiences by matching items with suitable users, addressing unique challenges like user availability, and demonstrating significant real-world benefits in a large-scale video platform.
Contribution
The paper presents DualRec, a novel creator-side recommendation approach that adapts existing algorithms and introduces a user availability module, significantly enhancing creator engagement in large-scale systems.
Findings
DualRec improves creator engagement in Kwai's platform.
Adapting user-side algorithms simplifies creator-side recommendation design.
User availability calculation enhances system performance.
Abstract
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…
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Taxonomy
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