Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users' preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don't like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different level of exploration control and users' subjective preferences on interfaces are more predictive of their satisfaction with the recommender.

翻译：推荐系统常常难以在匹配用户偏好与提供意外推荐之间取得平衡。当推荐过于狭窄且未能覆盖用户的全部偏好范围时，系统会被认为毫无用处。反之，当系统推荐过多用户不喜欢的项目时，则会被视为缺乏个性化或效果不佳。为了更好地理解用户对电影推荐系统推荐广度的主观感受，我们进行了访谈和问卷调查，发现许多用户认为狭窄的推荐很有用，而少数用户则明确希望获得更大的推荐广度。此外，我们设计并开展了一项针对更大用户群体的在线现场实验，评估了两种旨在为用户提供更广泛推荐访问权限的新界面。我们考察了两类用户（初始电影多样性较高和较低的用户）的偏好与行为。研究结果发现，不同程度的探索控制以及用户对界面的主观偏好更能预测其对推荐系统的满意度。