Recommending Products and Services Belonging to Online Businesses Using Intelligent Agents

Abstract

A sure method for a business organization to sell more products is to expand its customer base and to have its products recommended by other organizations and individuals. This paper takes a look at the techniques used by shopping websites in order to entice the user in purchasing their products, and proposes a system for recommending products and services provided by different online businesses to potential customers. The solution is built upon a service-oriented architecture that allows businesses to share information regarding customers’ purchases while taking into account the user privacy issue. Intelligent agents, which rely on a product type association dynamically weighted graph, are employed in order to obtain and to process the information needed to make the suggestions. The use of intelligent agents significantly improves the quality of the recommendations made by the system. This improvement is achieved by suggesting products and services depending on other users’ purchasing patterns while also considering the different product types and quantities sold by the business organizations that are part of the system.

Impact of artificial intelligence adoption on online returns policies

“…Leung et al (2018) examine how artificial intelligence automation removes the possibility for consumers to internalize the outcomes of the consumption experience. Alexandrescu et al (2017) find that employing artificial intelligence agents that rely on dynamically weighted graphs can effectively recommend better fit products. Sivamani et al (2018) examine a decision support system for the nutritional management of livestock using the Bayesian model based on fuzzy rules.…”

“…Using data-driven recommendation algorithms can result in a nearly 14% increase in profits for an online retailer (Ettl et al 2019). In addition, employing intelligent recommendation agents with collaborative filtering can effectively recommend better fit products (Alexandrescu et al 2017). Although these artificial intelligence applications cannot entirely eliminate fit uncertainty caused by nondigital attributes of products, creating an offline showroom allows consumers to ascertain product fit information prior to making a purchase.…”