AI-BASED PRODUCT RECOMMENDATION SYSTEM FOR PERSONALIZED RECOMMENDATION

Abstract

Personalized products recommendations have become essential in increasing customer satisfaction, interactions and sales in the dynamic world of e-commerce. Traditional recommendation techniques, such as collaborative filtering, content-based filtering, etc. are prone to failure on the problems of data sparseness, cold-start problem and scalability. The vision presented in this work is a high-tech AI-based product recommendation system, which embraces the hybrid models that combine collaborative-filtering and content-based-filtering, machine learning and deep-learning.

techniques. The system aims to improve recommendation accuracy by analysing live user behavior, fixing cold-start issues and be transparent with explainable AI (XAI).

Based on Amazon product review data, the article uses Singular Value Decomposition (SVD) in place of collaborative filtering and TF-IDF in place of content-based filtering, achieving a precision at ten of 78 percent in the hybrid method. The findings show that the system is able to enhance user experience, business development and address ethical challenges including privacy of data and bias in algorithms. The future directions include the investigation of transformer-based models, multi-modal data integration and fairness-conscious algorithms.

Other important.