Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available dataset containing user feedback regarding serendipity. We compared our SOG algorithm with topic diversification, popularity baseline, singular value decomposition, serendipitous personalized ranking and Zheng’s algorithms relying on the above dataset. SOG outperforms other algorithms in terms of serendipity and diversity. It also outperforms serendipity-oriented algorithms in terms of accuracy, but underperforms accuracy-oriented algorithms in terms of accuracy. We found that the increase of diversity can hurt accuracy and harm or improve serendipity depending on the size of diversity increase.

中文翻译：

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偶然性如何影响推荐系统的多样性？一种面向意外的贪心算法

大多数推荐系统会推荐在所有用户中流行且与用户通常消费的项目相似的项目。结果，用户收到她/他已经熟悉或无论如何都会找到的推荐，导致低满意度。为了克服这个问题，推荐系统应该建议新颖的、相关的和意想不到的，即偶然的项目。在本文中，我们提出了一种面向意外发现的重排序算法，称为面向意外发现的贪婪（SOG）算法，该算法通过特征多样化提高了推荐的意外发现，并有助于克服过度专业化的问题。为了评估我们的算法，我们采用了唯一公开可用的数据集，其中包含用户关于意外的反馈。我们将我们的 SOG 算法与主题多样化、流行基线、奇异值分解、偶然的个性化排名和郑的算法依赖于上述数据集。SOG 在偶然性和多样性方面优于其他算法。它在准确性方面也优于面向偶然性的算法，但在准确性方面不如面向准确性的算法。我们发现，多样性的增加会损害准确性，并根据多样性增加的大小损害或提高偶然性。