Abstract
Information overload affects the efficiency of resource utilization. To tackle the problems, book recommendation is one of the solutions for university libraries which possess huge volumes of books and reading-intensive users. The users borrow books mainly for course-learning and academic-studying, which means the recommendation strategy should consider not only personalization but also the commonness driven by curricular necessity. This paper first studies the users behaviors on a large scale book-loan logs of a university library; then implements two recommendation algorithms based on the book-loan data, one of which is the classical item-based cooperation filtering recommendation algorithm, the other is a probability-based algorithm proposed in this paper. The average precision of the probability-based algorithm performs better in a random sampled testing set. The paper finally discusses the application cases of different algorithms in university libraries’ routine work.
The paper is supported by National Natural Science Foundation of China 70903008, China MOE Humanities and Social Sciences project 11YJC870010, Fundamental Research Funds for the Central Universities of China (2011 Beijing Normal University project) and National Key Technologies R&D Program of China 2012BAH01F01-03.
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Chen, C., Zhang, L., Qiao, H., Wang, S., Liu, Y., Qiu, X. (2012). Book Recommendation Based on Book-Loan Logs. In: Chen, HH., Chowdhury, G. (eds) The Outreach of Digital Libraries: A Globalized Resource Network. ICADL 2012. Lecture Notes in Computer Science, vol 7634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34752-8_33
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DOI: https://doi.org/10.1007/978-3-642-34752-8_33
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