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An Improved Personalized Recommendation Technique for E-Commerce Portal  

Ko, Pyung-Kwan (인하대학교 컴퓨터정보공학부)
Ahmed, Shekel (인하대학교 컴퓨터정보공학부)
Kim, Young-Kuk (충남대학교 컴퓨터공학과)
Kamg, Sang-Gil (인하대학교 컴퓨터정보공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.14, no.9, 2008 , pp. 835-840 More about this Journal
Abstract
This paper proposes an enhanced recommendation technique for personalized e-commerce portal analyzing various attitudes of customer. The attitudes are classifies into three types such as "purchasing product", "adding product to shopping cart", and "viewing the product information". We implicitly track customer attitude to estimate the rating of products for recommending products. We classified user groups which have similar preference for each item using implicit user behavior. The preference similarity is estimated using the Cross Correlation Coefficient. Our recommendation technique shows a high degree of accuracy as we use age and gender to group the customers with similar preference. In the experimental section, we show that our method can provide better performance than other traditional recommender system in terms of accuracy.
Keywords
collaborative filtering; cross correlation coefficient; personalization; recommendation technique; scalability;
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