Bake Two Cakes with One Oven: RL for Defusing Popularity Bias and Cold-start in Third-Party Library Recommendations
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| Format: | Preprint |
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2025
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| _version_ | 1866912334405959680 |
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| author | Vuong, Minh Hoang Bui, Anh M. T. Nguyen, Phuong T. Di Ruscio, Davide |
| author_facet | Vuong, Minh Hoang Bui, Anh M. T. Nguyen, Phuong T. Di Ruscio, Davide |
| contents | Third-party libraries (TPLs) have become an integral part of modern software development, enhancing developer productivity and accelerating time-to-market. However, identifying suitable candidates from a rapidly growing and continuously evolving collection of TPLs remains a challenging task. TPL recommender systems have been studied, offering a promising solution to address this issue. They typically rely on collaborative filtering (CF) that exploits a two-dimensional project-library matrix (user-item in general context of recommendation) when making recommendations. We have noticed that CF-based approaches often encounter two challenges: (i) a tendency to recommend popular items more frequently, making them even more dominant, a phenomenon known as popularity bias, and (ii) difficulty in generating recommendations for new users or items due to limited user-item interactions, commonly referred to as the cold-start problem. In this paper, we propose a reinforcement learning (RL)-based approach to address popularity bias and the cold-start problem in TPL recommendation. Our method comprises three key components. First, we utilize a graph convolution network (GCN)-based embedding model to learn user preferences and user-item interactions, allowing us to capture complex relationships within interaction subgraphs and effectively represent new user/item embeddings. Second, we introduce an aggregation operator to generate a representative embedding from user and item embeddings, which is then used to model cold-start users. Finally, we adopt a model-based RL framework for TPL recommendation, where popularity bias is mitigated through a carefully designed reward function and a rarity-based replay buffer partitioning strategy. The results demonstrated that our proposed approach outperforms state-of-the-art models in cold-start scenarios while effectively mitigating the impact of popularity bias. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bake Two Cakes with One Oven: RL for Defusing Popularity Bias and Cold-start in Third-Party Library Recommendations Vuong, Minh Hoang Bui, Anh M. T. Nguyen, Phuong T. Di Ruscio, Davide Software Engineering Third-party libraries (TPLs) have become an integral part of modern software development, enhancing developer productivity and accelerating time-to-market. However, identifying suitable candidates from a rapidly growing and continuously evolving collection of TPLs remains a challenging task. TPL recommender systems have been studied, offering a promising solution to address this issue. They typically rely on collaborative filtering (CF) that exploits a two-dimensional project-library matrix (user-item in general context of recommendation) when making recommendations. We have noticed that CF-based approaches often encounter two challenges: (i) a tendency to recommend popular items more frequently, making them even more dominant, a phenomenon known as popularity bias, and (ii) difficulty in generating recommendations for new users or items due to limited user-item interactions, commonly referred to as the cold-start problem. In this paper, we propose a reinforcement learning (RL)-based approach to address popularity bias and the cold-start problem in TPL recommendation. Our method comprises three key components. First, we utilize a graph convolution network (GCN)-based embedding model to learn user preferences and user-item interactions, allowing us to capture complex relationships within interaction subgraphs and effectively represent new user/item embeddings. Second, we introduce an aggregation operator to generate a representative embedding from user and item embeddings, which is then used to model cold-start users. Finally, we adopt a model-based RL framework for TPL recommendation, where popularity bias is mitigated through a carefully designed reward function and a rarity-based replay buffer partitioning strategy. The results demonstrated that our proposed approach outperforms state-of-the-art models in cold-start scenarios while effectively mitigating the impact of popularity bias. |
| title | Bake Two Cakes with One Oven: RL for Defusing Popularity Bias and Cold-start in Third-Party Library Recommendations |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2504.13772 |