Multi Objective Recommender System for E-Commerce Platforms using Co-visitation Matrix

Multi Objective Recommender System for E-Commerce Platforms using Co-visitation Matrix

Kapil Mundada, Shreyash Jagtap, Atharva Jadhav , Shweta Jaybhaye, Makarand Jagadale

Department of Instrumentation and Control Engineering Vishwakarma Institute of Technology, Pune, 411037, Maharashtra, India

Abstract — With the rapid growth of e-commerce platforms, personalized recommendations have become crucial for enhancing user experience and driving customer engagement. However, traditional recommender systems often focus on optimizing a single objective, without considering the diverse goals and preferences of individual users. This paper proposes a novel multi-objective recommender system for e-commerce platforms that takes into account multiple objectives simultaneously. The system leverages a combination of user history, co-visitation matrices, and weighted multipliers to generate personalized recommendations. Experimental evaluations on real-world e-commerce datasets demonstrate the effectiveness and superiority of the proposed multi- objective recommender system compared to traditional approaches. The results show that the system achieves improved user satisfaction, increased engagement, and higher conversion rates.

Keywords —Recommender systems, E-commerce platforms, multi- objective optimization, Personalized recommendations, Co- visitation matrices