Decision support and data mining for direct consumer-to-consumer trading

This paper describes a decision support system that integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. The seller would provide the condition and number of years of usage of the used item, and the intelligent system would provide real-time search on related items in the marketplace and suggest a price for trading. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. Related items to a recently purchased item would also be suggested with an aim of providing friendly reminders and recommendations so that the user of the website would obtain a pleasant trading experience.