Context-Aware Fuzzy Recommender System for Sustainable Product Discovery: A Multi-Criteria Approach Using Statistical Aggregation Methods

The increasing environmental consciousness among consumers necessitates the development of intelligent recommendation systems that balance user preferences with sustainability goals.This research presents a novel Context-Aware Fuzzy Recommender System for Sustainable Product Discovery (CAFRS-SPD) that integrates contextual information, fuzzy logic reasoning and statistical aggregation methods (mean and median) to recommend environmentally responsible products.The proposed system addresses the critical gap in existing recommender systems that primarily focus on user satisfaction while neglecting environmental impact.Our methodology combines fuzzy membership functions with contextual factors such as temporal preferences, location-based constraints and user sustainability awareness levels.The system employs mean and median statistical measures for aggregating multiple sustainability criteria including carbon footprint, recyclability index and energy efficiency ratings.Experimental validation using the Amazon Product Dataset and MovieLens-25M dataset demonstrates that CAFRS-SPD achieves a 23.7% improvement in sustainability score while maintaining recommendation accuracy within 5.2% of traditional systems.The fuzzy inference engine successfully handles uncertainty in sustainability assessments while contextual adaptation ensures personalized recommendations aligned with individual user contexts.Comparative analysis with five baseline methods reveals superior performance in terms of sustainability awareness (F1-score: 0.847), contextual relevance (precision: 0.823) and user satisfaction (recall: 0.791).The statistical aggregation approach using weighted mean and robust median estimators effectively combines heterogeneous sustainability metrics, resulting in more reliable sustainability assessments.This research contributes to the growing field of green recommender systems by providing a comprehensive framework that promotes sustainable consumption patterns while preserving user experience quality.