AHRS: An enhanced hybrid and knowledge-driven recommendation system for movie dataset
Bhavana, Neha Dutta · 2025
The current environment relies heavily on recommendation systems to analyze customer behavior on social media, e-commerce, and online businesses. The Internet provides several sources of knowledge, giving users various suggestions and advice. This may make it difficult for the user to make an accurate decision and get confused in the competitive and developing sector. RS is a crucial e-commerce component since it delivers the knowledge that customers need selectively and appropriately. The major flaws in the current recommendation system are excessive suggestions and uncertainty regarding new products. Several systems for recommendation use customers’ purchasing history to suggest new things. In addition to the user&s;s past purchases, it is essential to examine their online behavior, reviews, list of desires, ratings, and already-purchased items. An enhanced and knowledge-driven recommendation system using a hybrid model, i.e., the AHRS model, to help customers avoid duplicate and irrelevant recommendations. The experimental findings show that the proposed recommendation system&s;s RMSE and MAE have significantly improved when compared to other conventional strategies.