Abstract
Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge—either hand-coded knowledge provided by experts or “mined” knowledge learned from the behavior of consumers—to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the additional knowledge required from the database, the ways the recommendations are presented to consumers, the technologies used to create the recommendations, and the level of personalization of the recommendations. We identify five commonly used E-commerce recommender application models, describe several open research problems in the field of recommender systems, and examine privacy implications of recommender systems technology.
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Schafer, J.B., Konstan, J.A., Riedl, J. (2001). E-Commerce Recommendation Applications. In: Kohavi, R., Provost, F. (eds) Applications of Data Mining to Electronic Commerce. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1627-9_6
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DOI: https://doi.org/10.1007/978-1-4615-1627-9_6
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