Survey on Diversified Recommendation

The recommender system has a significant role in alleviating information overload, allowing users to conveniently obtain products and services on various application platforms like Tmall, Douyin, and Xiaohongshu. However, most of the recommendation systems focus on the accuracy rate as the center, which leads to adverse effects such as the limitation of users' vision, fewer display opportunities for some merchants, a single content ecosystem of the platform, and an unbalanced allocation of resources and information, such as triggering the filter bubble and the Matthew effect. As a result, strengthening the diversity of the recommendation system has become a key research point to fulfill the increasingly diversified material demands in people's lives. In recent years, research on diversified recommendations has advanced rapidly. However, this aspect needs to be more systematic in organization and summarization. This paper systematically reviews the issue of diversified recommendations within recommendation systems. Firstly, we put forward the problem definition, technical framework, classification, and application scenarios of diversified recommendations. Secondly, we make comparisons and analyses of models and algorithms from four perspectives. Subsequently, we summarize the commonly used datasets and metrics for diversified recommendations. Finally, we deliberate on the problems and challenges in this field to inspire future innovation and promote development.