Applying Multimedia Data Mining to an Associative Classification-Based Recommendation System in Ecommerce

With increasing popularity of e-commerce, the growth potential of this business is extraordinary; however, the main challenge for such a paradigm originates from the difficult in finding the most valuable products to recommend customer appropriate information. One of the problems is that business managers generally lack sufficient information to identify whether the product descriptions and images in the website successfully attracted customers in browsing and buying the products. The objective of this research is to develop a recommendation mechanism using multimedia data mining algorithm. Basically, it analyses a large amount of multimedia images information to find the most valuable images and descriptions of products. Our findings show that these images and descriptions match customers' various demands. This paper use the color image semantics applied in the Munsell (HSV) color space and use the content-based image retrieval application to establish a recommendation rule based on an associative classification method for various product images in ecommerce. The prototype has been developed to evaluate the mechanism feasibility and is applied in a real e-commerce case. The result shows that it is useful for on-line sellers to make recommendation decisions. In order to further validate the practicability of this mechanism, the extensive case study with larger trading volumes in apparel e-business areas was conducted. This case study shows that the multimedia data mining approach is an automatic and effective solution, which can be used in marketing to discover consumer habits.