A large amount of research on recommender systems has focused on improving the accuracy of suggestions in offline settings. However, this focus and the commonly used techniques can lead to a “filter bubble”, severely limiting the diversity of content discovered by users. Several offline studies show that this can be mitigated by using experts for recommendation. In contrast to standard recommender systems, experts are able to generate more diverse recommendations and increase the novelty of given suggestions. They can be used in missing-data or cold-start scenarios and reduce noise in the users' ratings. This paper examines the impact of employed experts' recommendations on user behavior for a real-world recommender system on a popular video-on-demand website, provided by a large television network. We study whether the potential benefits of experts lead to differences in user behavior, user perceptions and properties of given recommendations (e.g., diversity). We find that enriching a state-of-the-art system with the suggestions of employed experts can significantly increase platform use. Even though expert recommendations are used less frequently and are less successful than expected, users watch a greater number of clips, use more recommendations, and come back to the website more frequently when they receive expert suggestions. When searching for other influencing factors, we find that experts generate more diverse recommendations and improve the taste coverage of the system keeping user satisfaction unaffected. In summary, our results show large benefits of using employed experts and have implications for the design and use of recommender systems in real-world scenarios.

对推荐系统的大量研究都集中在提高离线设置中建议的准确性上。但是，这种关注点和常用技术会导致“过滤泡”，严重限制了用户发现的内容的多样性。多项离线研究表明，可以通过聘请专家来缓解这种情况。与标准推荐系统相比，专家能够生成更多不同的建议，并增加给定建议的新颖性。它们可用于数据丢失或冷启动的情况，并降低用户额定值中的噪声。本文研究了由大型电视网络提供的受欢迎的视频点播网站上的真实推荐系统中，已聘用专家的推荐对用户行为的影响。我们研究专家的潜在利益是否会导致用户行为，用户认知和给定建议的属性（例如多样性）方面的差异。我们发现，通过聘用专家的建议来丰富最先进的系统可以大大增加平台的使用率。尽管专家建议的使用频率不高且未如预期那样成功，但用户观看大量剪辑，使用更多建议并在收到专家建议后更频繁地访问网站。在寻找其他影响因素时，我们发现专家会提出更多建议，并改善系统的口味覆盖范围，从而使用户满意度不受影响。综上所述，