| Summary: | 碩士 === 輔仁大學 === 資訊管理學系碩士班 === 103 === E-commerce often relies upon recommendation techniques given their potential commercial value, as well as recommendation systems’ capacity for accurate prediction that can boost websites’ conversion rates, promote the electronic trade of goods, and increase company sales. For these systems, collaborative filtering (CF) enables websites to recommend products for target users based on the preferences of peers with similar interests. While CF can expand a user’s profile of interests, it cannot overcome the problems of cold start and sparse ratings (i.e., an individual can vote only for a small fraction of all items). Since recent studies have shown that the stability of users’ preferences influences their decision making, especially concerning experiential goods (e.g., movies, books), measuring such stability regarding these goods is worth investigating. This study thus proposes integrating genre- and director-based anchoring processes to identify users’ preferences for movie genres and measure preference stability in order to provide more precisely personalized recommendations. Specifically, we overcome the problem of sparse ratings by analyzing associations among movie genres as well as the correlations of the director and genre as means to pinpoint genre-based associations. By employing the analytic hierarchy process (AHP), we thus infer user preferences for movie genres via a series of interactive genre- and director-based anchoring processes, which ultimately provides effective, precisely interactive movie recommendations.
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