The product ranking model is the model to create the ranking lists over the product listing data obtained from the e-commerce portals. The product ranking models are utilized to produce the variety of product ranking lists in order to fulfill the requirement to show the multiple lists over the e-commerce ranking lists. The e-commerce portals use the variety of lists to increase the usability and the accessibility by creating the maximum visibility of the output results, which are shown for the similar product ranking, products suggested by other users, most selling products, newly added products, etc. A perfect product ranking model must be capable of producing the content-based and collaborative indexing over the input data. The content-based product ranking models are utilized to produce the product ranking by evaluating the input query by the users. The collaborative filtering is the algorithm used to create the product ranking lists according to the user’s profile and other similar users, friends, etc. In this thesis, the proposed model has been designed by using the effective combination of the content-based and collaborative filtering methods for the realization of the robust product ranking model.
Product ranking, e-commerce ranking, k-nearest neighbor, non-probabilistic classification