In multimodal-aware recommendation, the extraction of meaningful multimodal features is at the basis of high-quality recommendations. Generally, each recommendation framework implements its multimodal extraction procedures with specific strategies and tools. This is limiting for two reasons: (i) different extraction strategies do not ease the interdependence among multimodal recommendation frameworks; thus, they cannot be efficiently and fairly compared; (ii) given the large plethora of pre-trained deep learning models made available by different open source tools, model designers do not have access to shared interfaces to extract features. Motivated by the outlined aspects, we propose Ducho, a unified framework for the extraction of multimodal features in recommendation. By integrating three widely-adopted deep learning libraries as backends, namely, TensorFlow, PyTorch, and Transformers, we provide a shared interface to extract and process features where each backend's specific methods are abstracted to the end user. Noteworthy, the extraction pipeline is easily configurable with a YAML-based file where the user can specify, for each modality, the list of models (and their specific backends/parameters) to perform the extraction. Finally, to make Ducho accessible to the community, we build a public Docker image equipped with a ready-to-use CUDA environment and propose three demos to test its functionalities for different scenarios and tasks. The GitHub repository and the documentation is accessible at this link: https://github.com/sisinflab/Ducho.

翻译：在多模态感知推荐中，提取有意义的多模态特征是高质量推荐的基础。通常，各推荐框架采用特定策略和工具实现其多模态提取过程，这存在两方面的局限性：（i）不同的提取策略阻碍了多模态推荐框架间的相互依赖性，导致无法进行高效且公平的比较；（ii）尽管开源工具提供了大量预训练深度学习模型，但模型设计者缺乏共享接口来提取特征。基于上述问题，我们提出Ducho——一个面向推荐场景的多模态特征提取统一框架。通过集成三个广泛使用的深度学习库（TensorFlow、PyTorch和Transformers）作为后端，我们提供了共享接口用于特征提取与处理，将每个后端的特定方法对终端用户进行抽象。值得注意的是，该提取流程可通过YAML格式配置文件轻松定制，用户可为每种模态指定执行提取的模型列表及其对应的后端/参数。最后，为方便社区使用Ducho，我们构建了配备即用CUDA环境的公共Docker镜像，并提供三个演示案例以测试其在不同场景和任务中的功能。GitHub仓库及文档访问地址为：https://github.com/sisinflab/Ducho。