The perception of diversity in a set or sequence of recommendations (e.g., in news recommendation)

Idea

Research has found that good sequences of recommendations have a certain degree of diversity in the recommended items. For some domains, diversity is even more critical than for others. The music domain is an example: Having a playlist recommended with songs of just one artist might be boring for some users; having a playlist with the same song interpreted by different artists is also (in most cases) not what users are looking for. Diversity is needed. Yet, what is perceived as diverse is subjective.
This research aims to find what variables influence whether a sequence of recommendations is perceived as diverse or not (or to what degree).
A related question is: What are the essential aspects that have to be considered by algorithms to create a sequence of recommendations that is perceived as diverse?

Relevant references:

• Christoph Trattner & Dietmar Jannach (2020). Learning to recommend similar items from human judgments. User Modeling and User-Adapted Interaction, 30, 1–49. https://doi.org/10.1007/s11257-019-09245-4

• Mathias Jesse, Christine Bauer, & Dietmar Jannach (2022). Intra-list similarity and human diversity perceptions of recommendations: the details matter. User Modeling and User-Adapted Interaction, 33(4), 769–802. https://doi.org/10.1007/s11257-022-09351-w