| Summary: | 碩士 === 國立中正大學 === 資訊管理系研究所 === 104 === Many modern enterprise collect large quantities of data, such as customer preference or their temporal purchasing behaviors, and utilize data mining as their competitive advantages. However, some conflicts in the data may exist, and determining how to aggregate many different opinions into a consensus is a traditional core problem in recommendation systems and decision support systems. Taking the preference ranking problem as an example, u_1 : (A ≥ B ≻ C) indicates that for the user u_1, (1)A is at least favorable than B; (2)B is more favorable compared to C. Another temporal ranking problem is to discover the possible temporal relationships among items, which refer to the temporal ordering of items. For example, u_1 : (A < B = C) indicates that the user considers that item A should occur before B and item B can occur simultaneously with C. However, in the real world, user may have many different aspects of consideration in regards to the same itemset at the same time. The real-life application is that when investors purchase stocks, they consider not only the stock preference due to personal risk tolerance, but also the temporal order of stock investment to maximize the profit because of the temporal effects of supply chain positions. Based on the above ideas, this study defines a novel model and proposes its associated algorithm for discovering consensus patterns combining preference ranking and temporal sequence. A two-phase experiment was designed to collect authentic datasets, execute the algorithm for its effectiveness via user rating, and demonstrate its managerial meaning.
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