A recommender system is employed to accurately recommend items, which are expected to attract the user’s attention. The over-emphasis on the accuracy of the recommendations can cause information over-specialization and make recommendations boring and even predictable. Novelty and diversity are two partly useful solutions to these problems. However, novel and diverse recommendations cannot merely ensure that users are attracted since such recommendations may not be relevant to the user’s interests. Hence, it is necessary to consider other criteria, such as unexpectedness and relevance. Serendipity is a criterion for making appealing and useful recommendations. The usefulness of serendipitous recommendations is the main superiority of this criterion over novelty and diversity. The bulk of studies of recommender systems have focused on serendipity in recent years. Thus, a systematic literature review is conducted in this paper on previous studies of serendipity-oriented recommender systems. Accordingly, this paper focuses on the contextual convergence of serendipity definitions, datasets, serendipitous recommendation methods, and their evaluation techniques. Finally, the trends and existing potentials of the serendipity-oriented recommender systems are discussed for future studies. The results of the systematic literature review present that the quality and the quantity of articles in the serendipity-oriented recommender systems are progressing.

采用推荐器系统来准确地推荐期望引起用户注意的项目。过度强调建议的准确性可能导致信息过度专业化，并使建议枯燥甚至可预测。新颖性和多样性是解决这些问题的两个部分有用的解决方案。然而，新颖和多样化的推荐不能仅仅确保吸引用户，因为这样的推荐可能与用户的兴趣无关。因此，有必要考虑其他标准，例如意外性和相关性。偶然性是提出有吸引力和有用建议的标准。偶然推荐的有用之处是该标准相对于新颖性和多样性的主要优势。推荐系统的大部分研究近年来都集中在偶然性上。因此，本文针对面向偶然性推荐系统的先前研究进行了系统的文献综述。因此，本文重点关注偶然性定义，数据集，偶然性推荐方法及其评估技术的上下文融合。最后，讨论了偶然性推荐系统的趋势和现有潜力，以供将来研究。系统的文献综述的结果表明，在偶然性推荐系统中文章的质量和数量都在进步。本文着重于偶然性定义，数据集，偶然性推荐方法及其评估技术的上下文融合。最后，讨论了偶然性推荐系统的趋势和现有潜力，以供将来研究。系统的文献综述的结果表明，在偶然性推荐系统中文章的质量和数量都在进步。本文着重于偶然性定义，数据集，偶然性推荐方法及其评估技术的上下文融合。最后，讨论了偶然性推荐系统的趋势和现有潜力，以供将来研究。系统的文献综述的结果表明，在偶然性推荐系统中文章的质量和数量都在进步。