Product Recommendation Agents for Cyber Shopping Consumers

Abstract

With cyber shopping, consumers face a massive amount of product information before an educated purchase decision can be made. Identifying relevant products is therefore laborious for consumers, in particular when they look for non-commodity products such as consumer electronics. Product Recommendation Agents (PRAs) help consumers in finding relevant products efficiently. PRAs recommend a set of products either explicitly according to product attributes preferred by the consumer or implicitly based on consumers’ interests and activities. PRAs retrieve hereby product information from various sources such as a retailer’s product database or a third-party’s review database. This entry introduces and discusses PRAs for cyber shopping consumers from five perspectives: (1) Purchase decision-making, (2) natural language interaction, (3) dynamic pricing, (4) product reviews, and finally, (5) product recommendation infrastructures. Future research directions on PRAs for cyber shopping conclude this entry.