Web recommender systems play a significant role in different domains, such as movies, books, music, etc., and contributes to not only user satisfaction but also to e-business and e-commerce. It utilizes the user’s profile information, preferences, and activities for recommendations of different objects. However, the distinct nature of watching smart TV greatly affects the accuracy and efficiency of recommender systems due to the reasons that it is a lean-back and multi-user device. Hence, the predictions and calculations of a user’s profile, preferences, and activities cannot be accurately utilized by the recommender systems for recommendations to the exact viewer(s) watching smart TV. This paper presents a critical review of existing recommender systems in the perspectives of smart TV watching scenarios. It highlights the issues and challenges and presents some research opportunities to deal with it. It further presents a subjective study for validating the highlighted factors that affect the recommendation results specifically on a smart TV. Results show that watching activities on a smart TV is significantly different from other devices, such as smartphones and computers. It further shows that smart TV is a non-personalized device and normally enjoyed in groups. Hence, personalized recommendations on smart TV need further investigation. The paper concludes that the existing recommender systems need further improvement to cope with issues of recommendations on a non-personalized device i.e., smart TV. Improving the recommender system for smart TVs may contribute not only to the viewer’s satisfaction but also to the conversion rate.

Web推荐系统在不同的领域（例如电影，书籍，音乐等）中发挥着重要作用，不仅有助于提高用户满意度，而且还有助于电子商务和电子商务。它利用用户的配置文件信息，首选项和活动来推荐不同的对象。但是，观看智能电视的独特性质由于其是一种后备式多用户设备而极大地影响了推荐系统的准确性和效率。因此，推荐者系统不能准确地利用用户的概况，喜好和活动的预测和计算来向向观看智能电视的确切观众推荐。本文从智能电视观看场景的角度提出了对现有推荐系统的严格审查。它突出显示了问题和挑战，并提出了一些应对它的研究机会。它还提出了一项主观研究，以验证影响推荐结果的突出因素，特别是在智能电视上。结果表明，在智能电视上观看活动与其他设备（例如智能手机和计算机）有显着差异。它进一步表明，智能电视是一种非个性化的设备，通常可以成组欣赏。因此，关于智能电视的个性化推荐需要进一步调查。本文的结论是，现有的推荐系统需要进一步改进，以应对非个性化设备（例如智能电视）上的推荐问题。改进用于智能电视的推荐器系统不仅可以提高观众的满意度，而且可以提高转换率。