With the advancement of machine learning and artificial intelligence technologies, recommender systems have been increasingly used across a vast variety of platforms to efficiently and effectively match users with items. As application contexts become more diverse and complex, there is a growing need for more sophisticated recommendation techniques. One example is the composite item (for example, fashion outfit) recommendation where multiple levels of user preference information might be available and relevant. In this study, we propose JIMA, a joint interaction modeling approach that uses a single model to take advantage of all data from different levels of granularity and incorporate interactions to learn the complex relationships among lower-order (atomic item) and higher-order (composite item) user preferences as well as domain expertise (e.g., on the stylistic fit). We comprehensively evaluate the proposed method and compare it with advanced baselines through multiple simulation studies as well as with real data in both offline and online settings. The results consistently demonstrate the superior performance of the proposed approach.

翻译：随着机器学习和人工智能技术的进步，推荐系统已广泛应用于各类平台，以高效、精准地将用户与项目进行匹配。随着应用场景日益多样化和复杂化，对更先进的推荐技术的需求也在不断增长。组合项目（例如时尚穿搭）推荐就是一个例子，其中可能涉及并利用多层次的用户偏好信息。在本研究中，我们提出了JIMA，一种联合交互建模方法，该方法使用单一模型来利用来自不同粒度层级的所有数据，并通过整合交互来学习低阶（原子项目）与高阶（组合项目）用户偏好之间以及领域专业知识（例如，风格搭配）之间的复杂关系。我们通过多项模拟研究以及在离线和在线环境下的真实数据，对所提方法进行了全面评估，并与先进的基线方法进行了比较。结果一致表明，所提方法具有优越的性能。