The rapid expansion of Web APIs presents developers with significant challenges in selecting optimal API compositions. To address this issue, keyword-based API composition recommendation techniques have been proposed. However, these methods often suffer from popularity bias due to the influence of historical datasets and recommendation models. This bias leads to the disproportionate recommendation of popular APIs over less popular ones, potentially causing the Matthew effect and impeding the balanced development of the API ecosystem. Although several studies have identified and attempted to mitigate popularity bias, they have largely relied on static analysis without accounting for the dynamic nature of API recommendations. In this paper, we introduce a dynamic simulation framework for API composition recommendations, designed to explore the evolution of popularity bias within recommendation results, and propose a debiasing method for dynamic recommendations by combining the enhanced API correlation graph with the Determinantal Point Process (DPP) method. Finally, extensive experiments on real datasets show that the algorithm effectively alleviates the popularity bias problem while guaranteeing high recommendation accuracy.

Finding more specific subcategories within a larger category is the goal of fine-grained image classification (FGIC), and the key is to find local discriminative regions of visual features. Most existing methods use traditional convolutional operations to achieve fine-grained image classification. However, traditional convolution cannot extract multi-scale features of an image and existing methods are susceptible to interference from image background information. Therefore, to address the above problems, this paper proposes an FGIC model (Attention-PCNN) based on hybrid attention mechanism and pyramidal convolution. The model feeds the multi-scale features extracted by the pyramidal convolutional neural network into two branches capturing global and local information respectively. In particular, a hybrid attention mechanism is added to the branch capturing global information in order to reduce the interference of image background information and make the model pay more attention to the target region with fine-grained features. In addition, the mutual-channel loss (MC-LOSS) is introduced in the local information branch to capture fine-grained features. We evaluated the model on three publicly available datasets CUB-200-2011, Stanford Cars, FGVC-Aircraft, etc. Compared to the state-of-the-art methods, the results show that Attention-PCNN performs better.