| Summary: | 碩士 === 國立臺灣大學 === 國際企業學研究所 === 100 === In modern society, the technology progression and information explosion, lead Database Marketing to become an edged and meanwhile essential tool, with which companies conduct marketing. Using Recommendation System, especially, is a powerful method for companies to vigorously contact customers, and to maintain customer relationships.
Recently, Hierarchical Bayesian Statistic Models have been proven capable of predicting customers’ behavior or information precisely. HBSM utilize not only customers’ personal level of information to predict further relatives, but whole body’s overall level of information to supplement the insufficient of personal level. Through above processes, HBSM are able to predict customers’ further buying behaviors or value migration, etc.
In this study, data in the database of a famous online shop in Taiwan is to be used. With the Probit model of HBSM, we can set up customers’ exclusively personal predict model, and predict customers’ next-buying items. And we will retain the customers’ actually final purchased items, in order to measure accuracy of the model.
Moreover, the restriction of time interval will be introduced in this study. According to the memory retrieval theories, if the time interval between sequent buying behaviors is too long, then those behaviors could not be seen to be correlative. Therefore, above restriction will be used to sieve improper data out. Besides, the HB Probit methods will be compared about accuracy, with Conditional Probability method, and Market Base Analysis method.
The conclusion in this study is that we can distinguish the degree of thickness of items’ separation by correlation matrix method. If the companies need only rough category recommendation, then they should use convenient methods like MBA method; and if the companies would like to conduct detailed item recommendation, then they should go through HB Probit method to attain outcome of personal exclusives.
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