A linguistically asymmetric similarity decision model integrating item tendency for rating predictions

Abstract

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.