In the evolving landscape of e-commerce, personalizing user experience through recommendation systems has become a way to boost user satisfaction and engagement. However, small-scale e-commerce platforms struggle with significant challenges, including data sparsity and user anonymity. These issues make it hard to effectively implement recommendation systems, resulting in difficulty in recommending the right products to users. This study introduces an innovative Hybrid Recommendation System (HRS) to address challenges in e-commerce personalization caused by data sparsity and user anonymity. By blending multiple dimensions of the data into one unified system for producing recommendations, this system represents a notable advancement in web engineering for achieving personalized user experiences in the context of limited data. This research emphasizes the significance of innovative and tech-driven solutions in transforming small-scale e-commerce platforms, providing direction for future research and development in the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.