Methodology for Product Recommendation Based on User-System Interaction Data: A Case Study on Computer Systems E-Commerce Web Site

Abstract

Within the scope of this study, we developed a product recommendation methodology for customers by analyzing shopping behaviors based on user-system interaction data collected on Casper Computer Systems' website. To achieve the right product to the right customer objective, we predict customer interests using a collaborative filtering algorithm on collected data from previous customer activities. In turn, this minimizes prediction errors and enables better-personalized suggestions of computer system configuration. We took advantage of the implicit feedback approach while modeling customer behaviors if they liked or disliked a particular product. After customer behavior data is collected, we form the customer-product matrix and generate personalized product suggestions for each customer with the help of user-itembased collaborating filtering and item-item-based collaborating filtering algorithms. Customer-website interaction is considered a key input variable in creating personalized recommendations. Customers are supposed to use the website and leave interaction data regarding product configurations they're interested in. To prove the efficiency of this methodology, we developed a prototype application. The product suggestion success rate of the application is tested on datasets generated from log data of the Casper website. Performance results prove that the developed methodology is successful.