A novel technique of association rules to provide efficient recommendation services for E-Commerce environment is proposed in this paper, which is used to help online shop managers to increase profit and give associate product recommendation to online customers. In order to reach these two goals this technique should be based on profit and give a better recommendation to buyers. Here the profit-support association rule algorithm is presented, which uses a unique profit to generate a minimum support for every item and multiple minimum supports to mine association rules. Through several experiments, we have shown that these optimization techniques can yield significant performance improvement.
Association rules, data mining, information extraction, recommendation system.
Jiabei Dai is with the University of British Columbia, Canada (e-mail: email@example.com).
Bin Zeng is with the Department of Management at the University of Naval Engineering, China.
Jiabei Dai and Bin Zeng, "
An Association Rule Algorithm for Online e-Commerce Recommendation Service," Journal of Economics, Business and Management vol. 4, no. 10, pp.