Recommender system based on click stream data using association rule mining

In the most studies of the past, only purchase data of users were used in e-commerce recommender system, while navigational and behavioral pattern data were not utilized. However, Kim, Yum, Song, and Kim (2005) developed a collaborative filtering technique based on navigational and behavioral patterns of customers in e-commerce sites. In this article, we improve on Kim et al. (2005) methods and further develop a novel recommender system. The proposed system calculates the confidence levels between clicked products, between the products placed in the basket, and between purchased products, respectively, and then the preference level was estimated through the linear combination of the above three confidence levels. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site for compact disc albums. The results from the experimental study clearly showed that the proposed method is superior to Kim et al. (2005) method.

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