Neuro-Fuzzy Aggregation for Multi-Criteria Recommender Systems: Comparative Study with Fixed-Weight Averaging
Year: 2026
Type: conference-paper
Abstract: Recommender systems have proved effective in handling information overload by providing personalized predictions. However, most of these systems are single-criterion recommenders that rely on a single rating. Multi-criteria recommender systems have gained popularity recently because they provide more effective recommendations than single-criterion models. This paper presents a comparative analysis of two aggregation techniques: standard fixed-weight averaging and a novel neuro-fuzzy aggregation framework. The proposed method combines fuzzy linguistic representations with a neural-network-based aggregator to model complex, nonlinear relationships among criteria. To generate criterion-level predictions, we use various collaborative filtering techniques, including User-based Collaborative Filtering (UserCF), Item-based Collaborative Filtering (ItemCF), Singular Value Decomposition (SVD), SVD++ (SVD with implicit-feedback extension), Probabilistic Matrix Factorization (PMF), and Deep Matrix Factorization (DeepMF). These predictions are then processed through both aggregation strategies. Evaluation on two benchmark datasets—TripAdvisor and Yahoo! Movies—demonstrates that the neuro-fuzzy method consistently achieves lower RMSE and MAE, underscoring its capability to enhance predictive performance in multi-criteria recommendation settings.
Source: Procedia Computer Science
Institution: Bennett University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7167732030