Electronic commerce (EC) has become an important support for business and is regarded as an efficient system that connects suppliers with online users. Among the applications of EC, the recommender system is undoubtedly a popular Issue to make the best recommendation to the users. Even if tremendous approaches have been proposed to perfect the recommendation, a comprehensive module comprising of essential sub-modules of input profiles, a recommendation mechanism, and output of recommendations in the recommender system is still lacking. Besides, the fundamental issue of profit consideration for an EC company was not stressed in general terms. Therefore, this study aims to construct a recommender system with a strategy-oriented operation module regarding the above aspects. Under this module, an approach named Clique-Effects Collaborative Filtering (CECF) for predicting the consumer's purchase behavior was proposed and evaluated. Finally, we undertook our proposed module in a 3C retailer in Taiwan, and comparative studies on employing various strategies were demonstrated.