The e-commerce product ranking models are designed to handle the very large volumes of the data involved in the database. Formally, the multifactor ranking models are incorporated over the online portals, which are capable of producing the multivariate lists. The product lists are prepared on the basis of various features, which include the popularity, accessibility and trust based factors which are associated with the e-commerce products for the realization of the content-based filtering over the e-commerce portals. In this paper, the multivariate and multifactor ranking algorithm has been proposed in order to solve the problems related to the low entropy, duplication and unbalanced feature analysis. The proposed model design is entirely based upon the popularity, visitor density, number of customers and security analysis based factors of the e-commerce pages containing the product lists. The multifactor values are organized in the different columns containing the different kinds of information, which are converted to the normalized and compatible values to create the data uniformity. The proposed model offers the collaborative index based product ranking model over the dense e-commerce databases. The proposed model has been designed to use the collaborative filtering based upon the k-nearest neighbor algorithm. The proposed model has been undergone the various experiments for the performance evaluation based upon the time complexity, resource utilization and other similar factors.