A MultiCriteria Approach in ECommerce Product Ranking
Tapasya Choudhary, Vanshika Aggarwal, Prasuk Jain, Shivani Trivedi · 2023
The customer experience in e-commerce can be enhanced by the employment of strong recommender systems. They can help customers identify items that interest them and also help to increase sales. A recommender system type that is gaining popularity is the ranking-based recommender system. This type of system gives each product a numerical value based on several factors, such as the product's popularity, the customer's past purchasing patterns, and their evaluations of comparable products. We provide a novel ranking-based recommender system designed for e-commerce in this research. Our method combines content-based and collaborative filtering to rate products. We evaluate our system on an actual dataset of online purchases and show that it outperforms earlier ranking-based recommender systems. Therefore, the findings suggest that recommender systems with weightings based on rankings could be a helpful tool for improving e-commerce user experience. To satisfy the increasing needs of clients for more transparent solutions and the daily rise in data volume, new algorithms must be developed.