Hybrid Product Recommendation System using Popularity Based and Content-Based Filtering
Shaive Sharma, Kaveri Baishya, Manjusha Pandey, Siddharth Swarup Rautaray · 2023
Data analysis is a vital tool for contemporary decision-making, empowering businesses to gain valuable insights from extensive datasets. In the realm of e-commerce, data analysis plays a crucial role in developing robust product recommendation systems that enhance user experiences and customer satisfaction. Through data analysis, businesses acquire a deeper understanding of customer behavior and preferences, enabling personalized recommendations for individual users. Advanced analytical techniques reveal hidden patterns and correlations, providing invaluable insights into customer preferences and purchasing behavior. This knowledge empowers businesses to deliver personalized recommendations tailored to individual needs and preferences. However, relying solely on rating-based recommendations has its limitations, as user ratings may not always accurately represent true customer preferences, neglecting factors like product profiling, customer demographics, and context. Hybrid recommendation systems have become a more practical solution to these restrictions. By integrating techniques like popularity-based filtering and content-based filtering, hybrid systems leverage the strengths of multiple methods to offer more accurate and diverse product recommendations. These systems incorporate data analysis techniques and consider product profiling to analyze a broader range of customer preferences, resulting in more relevant and satisfactory recommendations.