A PRODUCT REMCOMMENDATION SYSTEM USING BERT4REC MODEL.

Abstract

The rapid growth of data requires systems capable of extracting relevant insights and delivering effective recommendations. Traditional recommendation approaches often face challenges in understanding the relationships and contextual dependencies within large datasets. This creates a need for more robust methods to address these limitations. BERT (Bidirectional Encoder Representations from Transformers) is a language model designed to capture contextual and semantic relationships in text. Its bidirectional processing enables a better understanding of data compared to conventional methods. Integrating BERT into recommendation systems can improve the accuracy and relevance of recommendations by leveraging its capacity for deep context understanding. This study examines the use of BERT in recommendation systems, focusing on its ability to address challenges in traditional approaches. The research evaluates the performance of BERT-based models and their impact on recommendation accuracy and system effectiveness. The findings aim to provide insights into the use of advanced language models for improving recommendation systems.