A new recommender system to combine content-based and collaborative filtering systems
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Byung-Do Kim is Assistant Professor of Marketing at the School of Business Administration, Seoul National University, Korea. He was previously on the faculty of Carnegie Mellon University, Pittsburgh, USA. His current research interests include various econometric and statistical modelling issues on consumer choice behaviour, e-commerce, reward programmes and database marketing. His previous research has appeared in Journal of Business & Economic Statistics, Journal of Interactive Marketing, Journal of Marketing Research, Journal of Retailing, Marketing Letters and Marketing Science, among others.
Sun-Ok Kim is a doctoral candidate at the School of Business Administration, Seoul National University, Korea. She received her BBA from Yonsei University, Korea and received her MBA from Seoul National University, Korea. Her current research interests include recommender systems, consumer choice modelling, database marketing and retailing.
Abstract The enormous number of choices often create confusion for consumers so they often like to get the opinion of other people in order to make better buying decisions. Many e-commerce sites are implementing recommender systems to help their customers find the most valuable products and services. There are two fundamentally different approaches, the content-based and collaborative filtering techniques, to recommend products to customers based on their historical preferences. A new recommendation algorithm to combine these two systems is proposed in this paper. Applying the model to film rating data, the model is shown to perform better than the previous recommendation models in terms of predictive accuracy. How the model can be applied to personalise Internet shopping based on customer’s transaction history is also discussed.
Byung-Do Kim Seoul National University, School of Business Administration, 56-1 Shinlim-dong, Kwanak-ku, Seoul, 151-742, Korea. Tel: 82-2-880-8258; Fax: 82-2-878-3154; e-mail: [email protected]
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INTRODUCTION Consumers use the evaluation or opinion of other people as an important information source.1 People like to get recommendations when they perceive a risk in making a purchase decision or when they want to simplify their buying decision. For instance, when a consumer buys a camcorder, the consumer may ask their friends who have knowledge or experience of camcorders, or they may ask a salesperson to help them buy the best camcorder.
Journal of Database Marketing
Vol. 8, 3, 244–252
Recommendation becomes even more important in the Internet-based shopping environment where consumers do not make physical contact with products and face higher cognitive risk. In addition, e-commerce sites offer a very large number of alternatives since they do not have any physical constraint on inventory or shelf space. Hence, consumers may be confused by the number of choices. If the consumer is not familiar with the Internet, the problem becomes even more serious. In order to solve these
䉷 Henry Stewart Publications 1350-2328 (2001)
A new recommender system to
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