New Explainable Overlapping Co-Clustering for Recommender Systems: Capturing Multifaceted Preferences with Enhanced Interpretability

Recommender systems have become critical tools in reducing information overload by providing personalized recommendations across several application domains including commerce, industry, education, academic research, etc. Clustering-based recommender systems, which use the clustering technique to group similar users or items to generate suggestions, have shown high accuracy and efficiency. However, conventional clustering methods often fail to address several challenges such as ignoring the possibility that a user may have different item preferences, limited interpretability of generated suggestions, and the inability to tailor recommendation list sizes to individual user needs. To address all these issues, we propose in this work a new recommender system based on Overlapping Co-clustering and Modularity Maximisation (OCCMM). The proposed method allows to take into account that users may have several item preferences by building overlapping clusters rather than the conventional non-overlapping model. Also, the proposed method adopts a simultaneous clustering of items and users to facilitate the generation and interpretation of suggestions through using the co-clustering technique. Furthermore, OCCMM enables an adjustment of recommendation list sizes based on an easy tunning parameter δ. Experiments conducted in three real-world datasets demonstrated the effectiveness of OCCMM in achieving better performance in terms of accuracy and interpretability compared to conventional existing methods.