| Summary: | 碩士 === 國立中山大學 === 資訊管理學系研究所 === 89 === Abstract
Libraries have long been widely recognized as import information-offering institutes. Thousands of new books are acquired per month by our university—a mid-sized university in Taiwan), and patrons may have difficulties identifying the small set of books that really interest them. This gives rise to the problem of finding an effective way to recommend patrons the newly arrived books in a library. In this work, we address this problem in finding generalized association rules between patrons and books. We first discuss how to identify relevant but independent patron attributes in regard of the books they checked out. Then, we propose a set of algorithms for generating large itemsets and evaluate their performance experimentally. In addition, we define interestingness of rules and propose an algorithm for pruning uninteresting rules. Finally, we apply our approach to the circulation data of National SUN Yat-Sen University library and report our experiences.
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