Abstract
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, a Recommender System (RS) is undoubtedly a popular approach for promoting the products actively to the users. Even if many approaches have been proposed, a comprehensive module comprising of essential sub-modules of input profiles, a recommendation scheme, and an output interface of recommendations in the RS is still lacking. Besides, many approaches are confronted with the cold-start problem, which can be attributed to the problem of sparse user-item matrices. In addition, the fundamental issue of profit consideration for an EC company is not addressed in general terms. Therefore, this thesis aims to construct an RS with a strategy-oriented operation module regarding the above aspects; and with this module, three sub-modules of input, association prediction and output are proposed along with three tools of the Expectation-Robust Principal Component Analysis (E-RPCA), the Clique-Effects Collaborative Filtering (CECF), and the Strategy Analysis Model (SAM). The proposed RS module has been implemented by several experiments and the case studies of 3C retailers in Taiwan; promising results were obtained to approve the contributions of the proposed module.