Zhao YingSi, Shen Bo
School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China.
School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, 100044, China.
PLoS One. 2016 Jan 6;11(1):e0146541. doi: 10.1371/journal.pone.0146541. eCollection 2016.
User preference plays a prominent role in many fields, including electronic commerce, social opinion, and Internet search engines. Particularly in recommender systems, it directly influences the accuracy of the recommendation. Though many methods have been presented, most of these have only focused on how to improve the recommendation results. In this paper, we introduce an empirical study of user preferences based on a set of rating data about movies. We develop a simple statistical method to investigate the characteristics of user preferences. We find that the movies have potential characteristics of closure, which results in the formation of numerous cliques with a power-law size distribution. We also find that a user related to a small clique always has similar opinions on the movies in this clique. Then, we suggest a user preference model, which can eliminate the predictions that are considered to be impracticable. Numerical results show that the model can reflect user preference with remarkable accuracy when data elimination is allowed, and random factors in the rating data make prediction error inevitable. In further research, we will investigate many other rating data sets to examine the universality of our findings.
用户偏好在包括电子商务、社会舆论和互联网搜索引擎在内的许多领域都发挥着重要作用。特别是在推荐系统中，它直接影响推荐的准确性。尽管已经提出了许多方法，但其中大多数仅专注于如何改善推荐结果。在本文中，我们基于一组关于电影的评分数据介绍了一项关于用户偏好的实证研究。我们开发了一种简单的统计方法来研究用户偏好的特征。我们发现电影具有封闭性的潜在特征，这导致形成了许多具有幂律规模分布的小团体。我们还发现，与一个小团体相关的用户对该小团体中的电影总是有相似的看法。然后，我们提出了一个用户偏好模型，该模型可以消除被认为不可行的预测。数值结果表明，当允许数据消除时，该模型能够以显著的准确性反映用户偏好，并且评分数据中的随机因素使得预测误差不可避免。在进一步的研究中，我们将研究许多其他评分数据集，以检验我们研究结果的普遍性。
