Model of personalized recommendations without prior user data using FUZZY AHP

The rapid development of e-commerce necessitates the creation of recommendation systems capable of functioning effectively without prior user data. This paper explores an approach to generating personalized recommendations based on the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). The proposed model combines multi-criteria decision-making with the ability to integrate both objective product parameters and subjective user preferences, determined through pairwise comparisons of criteria. A distinctive feature of the approach is its ability to overcome the “cold start” problem. The presented methodology can serve as a foundation for developing modern intelligent decision-support systems in e-commerce. An experimental study confirmed the effectiveness of the model for personalized product ranking. Fig: 4. Tabl.: 7. Refs.: 10 items.