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Title:
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ON THE RECOMMENDER SYSTEM FOR UNIVERSITY LIBRARY |
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Author(s):
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Shunkai Fu, Yao Zhang, Seinminn |
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ISBN:
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978-972-8939-88-5 |
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Editors:
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Miguel Baptista Nunes and Maggie McPherson |
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Year:
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2013 |
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Edition:
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Single |
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Keywords:
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Recommender system, university library, collaborative filtering, user modeling |
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Type:
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Full Paper |
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First Page:
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215 |
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Last Page:
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222 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Libraries are important to universities, and they have two primary features: readers as well as collections are highly professional. In this study, based on the experimental study with five millions of usersÂ’ borrowing records, our discussion covers: (1) the necessity of recommender system for university libraries; (2) collaborative filtering (CF) technique is applicable and feasible; (3) user-based CF technique is preferred over item-based; (4) the performance of applying classical used-based collaborative filtering algorithm; (5) the effectiveness of local recommendation and the great saving of computing resource it may bring potentially. Since the data size used in our experiments is the largest one among similar studies, it is believed a valuable reference on this specific direction. |
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