Neighbourhood-based collaborative filtering (CF) methods typically rely only on user rating information for similarity calculation, without considering linguistic concepts (terms) that reflect user fuzzy preferences. However, in real-world decision-making processes, users often prefer to express their preferences for items linguistically rather than numerically. Inspired by this, we propose a probabilistic linguistic term set–based item similarity method that transforms absolute ratings into linguistic terms to capture the degree of importance users place on explicit aspects and opinions. Furthermore, we take into account the positive impact of users’ preferred consistency towards items on similarity results and introduce a Bhattacharyya coefficient–based item tendency to adjust semantic similarities, enhancing the reliability of predictions. In addition, we account for the asymmetric relation between items when selecting appropriate neighbours to optimise rating predictions. The experiments on two benchmark data sets indicate that our method outperforms existing similarity methods across various evaluation metrics. Specifically, compared with the state-of-the-art method, intuitionistic fuzzy set–based hybrid similarity model (IFS-HSM), the proposed model improves the performance by at least 2.1% and 1.9%, respectively, within the metrics mean absolute error ( MAE) and F1. Moreover, our approach provides a new insight for measuring similarity between items from both qualitative and quantitative perspectives.

中文翻译：

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集成项目倾向进行评分预测的语言不对称相似性决策模型

基于邻域的协同过滤（CF）方法通常仅依赖于用户评分信息来进行相似度计算，而不考虑反映用户模糊偏好的语言概念（术语）。然而，在现实世界的决策过程中，用户通常更喜欢用语言而不是数字来表达他们对项目的偏好。受此启发，我们提出了一种基于概率语言术语集的项目相似性方法，将绝对评级转换为语言术语，以捕获用户对明确方面和意见的重视程度。此外，我们考虑到用户对项目的偏好一致性对相似性结果的积极影响，并引入基于 Bhattacharyya 系数的项目倾向来调整语义相似性，从而增强预测的可靠性。此外，在选择适当的邻居来优化评分预测时，我们会考虑项目之间的不对称关系。在两个基准数据集上的实验表明，我们的方法在各种评估指标上都优于现有的相似性方法。具体来说，与最先进的方法、基于直觉模糊集的混合相似性模型（IFS-HSM）相比，所提出的模型在指标平均绝对值内分别将性能提高了至少 2.1% 和 1.9%误差（MAE）和F1。此外，我们的方法为从定性和定量角度衡量项目之间的相似性提供了新的见解。