## 2 Background  
### Diversity in Recommender Systems  
Accuracy has traditionally been considered an important criterion in the design of recommender systems. However, overfitting and hyper-personalization issues occur because of a limited range of options based on recommendation algorithms [6]. Several studies have demonstrated the value of diversity in reducing monotony and fostering better recommendations. Diversity is often described as being the opposite of similarity [7].  
A higher level of diversity is associated with a lower level of accuracy due to the trade-off between the two. Although conventional recommendation algorithms still prioritize accuracy over diversity, exposing viewers to diverse content is becoming increasingly important. [PERSON] et al. [8] stated that users' overall satisfaction with recommendation lists depends not only on accuracy but also on diversity. [PERSON] et al. [9] reported that a recommender system with high diversity makes users to explore various alternative options rather than to receive recommended results passively. In addition, [PERSON] et al. [10] found that system performance and perceived recommendation variety are positively correlated. [PERSON] et al. [11] demonstrated that recommendation diversity has a positive influence on users' choice and satisfaction.  
As diversity has been found to be an influential factor in recommendation quality, several studies have examined its effect by focusing on algorithmic manipulations. [PERSON] and [PERSON] [12] developed a novel method to increase the diversity of recommendations wherein users could adjust the diversity level they wanted. [PERSON] et al. [13] suggested re-ranking approaches as a personalized diversification method to solve a long-tail problem. [PERSON] et al. [14] addressed the dilemma between accuracy and diversity and proposed a new collaborative filtering-based recommendation method by employing an approximate nearest neighbor (ANN) search method called locality-sensitive hashing (LSH). [PERSON] and [PERSON] [15] developed a flexible recommender system using the trade-off between diversity and similarity. The proposed system regulates the diversity/similarity ratios depending on the sizes of the recommendation sets.  
However, owing to limited human cognitive capability, users cannot perceive diversity beyond a certain level. Only algorithmic efforts are insufficient for recommendation efficacy. There has been a demand for interface designs that help users recognize recommendation diversity. However, only a few studies have addressed this issue. [PERSON] and [PERSON] [16] developed an organization-based interface to encourage users to find diverse items. The study's findings showed that users gained more helpful and supportive information when they thought diversity was better, which gave them more confidence in their choices. This reiterates the importance of a recommendation interface, which has often been ignored compared with algorithmic perspectives. [PERSON] and [PERSON] [17] proposed a visual diversity-promoting interface for a better perception of diversity. The findings showed that the visual interface could help users perceive or become aware of recommendation diversity. Similarly, our study explores the manner in which diversity can be promoted in SVOD interfaces and examines its implications on information quality and consumers' intention to use services.