| Summary: | 碩士 === 國立中央大學 === 資訊管理學系 === 106 === Many people now search for medical services based on electronic word-of-mouth (eWOM). However, with the vast amount of information existing on the internet, finding the information that meets one’s needs is extremely time-consuming, and recommender systems constitute one of the important solutions to this issue. In the design of recommender systems, cold start (in which an item in a dataset is difficult to recommend when said item has not been rated by users) is a problem in urgent need of a solution because it significantly reduces the accuracy of recommendations. In view of this, we used eWOM and the Andersen Healthcare Utilization Model to develop a hybrid recommendation mechanism combining collaborative filtering (CF) and content-based filtering (CBF). The objective of our system, called the Dental Care Recommender (DCR), is to solve the cold-start problem and help users in making good decisions quickly.
To understand the usefulness and accuracy of the DCR, we recruited 50 participants to use the DCR and the Google search engine and compare the two. The results indicated that the recommendations made by the DCR were significantly more accurate than those made by Google. The participants also expressed significantly greater satisfaction with the search time, system quality, system performance, and system perception in the DCR than Google and significantly greater willingness to use the DCR (p<0.05). The results of this study can provide crucial reference to medical service providers and managers.
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