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Electronic business (e-business) uses information and communication technologies to conduct many activities of a traditional business process. E-business is usually done through the Internet and intranets. As a part of e-business, electronic commerce (e-commerce) is the process of buying and selling goods and services. E-commerce evolved to deal with all types of business interactions, including those between businesses and consumers (B2C e-commerce) and between consumers (C2C e-commerce). E-commerce, which typically uses the World Wide Web (the Web) on the Internet, has grown exponentially. With the explosive growth of goods and services available on the Web through e-commerce, it has become increasingly difficult for consumers to find and purchase the right products or services.
Recommender Systems (RS) provide consumers with personalized recommendations of goods or services, and thus help consumers find relevant goods or services in the information overload (Resnick & Varian, 1997). Since it was first introduced in the mid-1990s, a variety of recommender systems have been developed and used in a variety of e-commerce application domains, including Amazon.com, BarnesandNoble.com, Netflix.com, mystrands.com, and Yahoo.com (Konstan, Miller, Maltz, Herlocker, Gordon & Riedl, 1997; Sarwar, Karypis, Konstan & Riedl, 2000; Schafer, Konstan & Riedl, 2001). Over the last decade, recommender systems have been proven useful in increasing sales and retaining consumers, and are considered as an effective personalization tool in the e-commerce environment (Sarwar, Karypis, Konstan & Riedl, 2000; Schafer, Konstan & Riedl, 2001; Adomavicius & Tuzhilin, 2005; Goy, Ardissono & Petrone, 2007; Jannach, Zanker, Felfernig & Friedrich, 2011; Ricci, Rokach, Shapira & Kantor, 2011).
The concept of recommender systems is interdisciplinary and based on various technologies. Though relatively new, recommender technologies have made significant progress. In this article, we present a brief overview of the field of personalized recommendations and recommender systems in the context of e-commerce. First, we characterize the personalized recommendation problem and present a unifying model of recommender systems. We then examine current major approaches to personalized recommendations within this unifying model and the applications of personalized recommendations. We conclude with emerging and future research trends and additional readings in the area of recommender systems.
Key Terms in this Chapter
Hybrid Recommender Systems: Recommender systems that recommends items by combining two or more methods together, including the content-based method, the collaborative filtering-based method, the demographic method and the knowledge-based method.
Recommender systems: Systems that provide consumers with personalized recommendations of goods or services and thus help consumers find relevant goods or services in the information overload.
User-to-User Collaborative Filtering: Collaborative method that is based on similar consumers and recommends a list of items that other consumer gave feedback similar to that provided by the target consumer.
Knowledge-Based Recommender Systems: Recommender systems that are based on knowledge and suggest items by reasoning about what items meet the target consumer’s needs.
Personalized Recommendation Problem: Given a target consumer, produce personalized recommendations of goods or services for the target consumer.
Item-to-Item Collaborative Filtering: Collaborative method that is based on similar items and recommends a list of items that are similar to the items that were given good feedback by the target consumer.
Content-Based Recommender Systems: Recommender systems that are based on content of items and recommend a list of items with similar content to that of the items that were given good feedback by the target consumer.
Electronic business (e-business): Activities of a traditional business process by using information and communication technologies through the Internet and intranets.
Collaborative Recommender Systems: Recommender systems that recommend items through consumer collaborations and are the most widely used and proven method of providing recommendations. There are two types: user-to-user collaborative filtering based on user-to-user similarity and item-to-item collaborative filtering based on item-to-item similarity.