Wang Qi, You YinLi, Wang Si
School of Industrial Design, Hubei University of Technology, Wuhan, 430068, China.
School of Humanities, Jianghan University, Wuhan, 430056, China.
Sci Rep. 2024 Aug 27;14(1):19871. doi: 10.1038/s41598-024-70203-1.
With the development of society, online reviews are increasingly becoming a crucial factor in decision-making. Especially for entertainment products such as movies, they are preferred for their affordability and high entertainment factor. Therefore, this paper proposes a movie recommendation model that considers user personalization using a probabilistic linguistic approach based on online reviews. Firstly, the method constructs a quantitative sentiment framework that transforms comments into a multi-granular probabilistic sentiment language. Secondly, we build the decision-making trial and evaluation laboratory (DEMATEL) method for probabilistic linguistic environments to explore interrelationships between product attributes, and improve the distance measure and score function to better integrate probabilistic linguistic information into DEMATEL weight calculations. Furthermore, to account for risk preferences, the model employs the extended TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to determine the ranking of alternatives. Finally, we design Douban movie ranking experiments to demonstrate the validity of the model. Compared with other methods, this paper incorporates the emotional tendency of movie attributes and user preference into the decision-making process leading to more reasonable results.
随着社会的发展，在线评论日益成为决策中的关键因素。特别是对于电影等娱乐产品，因其价格亲民且娱乐性强而备受青睐。因此，本文提出一种基于在线评论、使用概率语言方法考虑用户个性化的电影推荐模型。首先，该方法构建一个定量情感框架，将评论转化为多粒度概率情感语言。其次，我们为概率语言环境构建决策试验与评价实验室（DEMATEL）方法，以探究产品属性之间的相互关系，并改进距离度量和得分函数，以便更好地将概率语言信息整合到DEMATEL权重计算中。此外，为考虑风险偏好，该模型采用扩展的TODIM（葡萄牙语中交互式多准则决策的首字母缩写）方法来确定备选方案的排名。最后，我们设计豆瓣电影排名实验来证明该模型的有效性。与其他方法相比，本文将电影属性的情感倾向和用户偏好纳入决策过程，从而得出更合理的结果。
