Bake Two Cakes with One Oven: RL for Defusing Popularity Bias and Cold-start in Third-Party Library Recommendations

• URL: http://arxiv.org/abs/2504.13772v1
• Date: Fri, 18 Apr 2025 16:17:20 GMT
• Title: Bake Two Cakes with One Oven: RL for Defusing Popularity Bias and Cold-start in Third-Party Library Recommendations
• Authors: Minh Hoang Vuong, Anh M. T. Bui, Phuong T. Nguyen, Davide Di Ruscio,
• Abstract summary: Third-party libraries (TPLs) have become an integral part of modern software development, enhancing developer productivity and accelerating time-to-market.<n>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.<n>We propose a reinforcement learning (RL)-based approach to address popularity bias and the cold-start problem in TPL recommendation.
• Score: 5.874782446136913
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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