Recommendation Systems: Marketing Applications, Benefits and Risks

Too many choices and limited time are among the biggest challenges of the modern world. Technological advancements led to an incremental increase in company offerings, creating diverse consumer choices. However, these endless alternatives may cause information overload, choice overload, and decision difficulties. Fortunately, technology could also be used to solve the problems it creates. Companies can balance the consumers’ cognitive load and improve customer experience and decision process by presenting relevant, useful, and quality information and eliminating unnecessary information with the help of technology. Recommendation systems are one of these enabling technologies. As digital technologies evolve and are used to support human–computer interactions, artificial intelligence-based applications have emerged as a popular topic in industry and academia. These applications have become diversified and increasingly used in various digital services in recent years. Recommendation systems, which are intensively used to enhance personalized customer experiences and reduce information overload, are a type of these applications. Recommendation systems act as information filtering tools that offer consumers relevant, and personalized items, content, or information. Recommendation systems can learn from consumers’ browsing behavior and preferences in websites, platforms, or mobile applications and use this data to personalize service offerings. Recommendation systems include three models: content-based, collaborative, and hybrid systems. Its primary aim is to reduce the consumers’ effort and time spent searching for relevant information. Although this technology benefits both consumers and companies, risks are also involved. Therefore, this study aims to address recommendation systems and their marketing applications and evaluate their benefits and risks from a marketing perspective.