A Hybrid Approach for Personalized Tour Guide Service and Tip Estimation

• Main Authors: Chien-Fang Jung, 容健芳
• Other Authors: Wei-Lun Chang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/c355p2

碩士 === 淡江大學 === 企業管理學系碩士班 === 102 === The growth of service industry impacts the economic development nowadays. However, service heterogeneity is still one of the complex problems to maintain superior service quality. Existing researches attempted to discuss and solve the problem of unstable service quality caused by human beings, such as the appropriateness of job for service providers and education training of staff to standardize the process and control service quality. In addition, some literature investigated the role of customer in service delivering process and the gap between their expectation and perception. Nevertheless, a few researches emphasized on the effect of interaction between service providers and customers that may result in different level of service quality. This research proposes a new service process by utilizing self-organizing maps and collaborative filtering to form a hybrid approach (including choosing the service providers and the amounts of given tips) based on customer perception. Through the unsupervised learning network, self-organizing maps can cluster and discover the similar segments of customers. Next, we use collaborative filtering approach to predict new customer’s preference based on similar segments. The proposed approach can effectively forecast customer’s preferences among service providers and assign appropriate employee to serve. Based on customer service experience and the local culture for tips, we can calculate the appropriate amount of tips as recommendation for customers. To blend customer-oriented spirit into service process, the proposed method also can effectively improve the level of satisfaction of customers. The result shows average value of MAP (mean average value) is 81% and the maximum value of MAP is 85%, which is good for the experiment. The attributes of recommending employees can fit to customer preference more. In other words, our approach can effectively bridge the gap between customer expectation and perception. Finally, the result also reflects on the amount of suggesting tips (i.e., the more the employee can match to the preference, the more tips customer may give).