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We propose a method to recommend books through machine learning modules based on several features, including library loan records. We evaluated the most effective method among ones using (a) a Support Vector Machine (SVM), (b) Random Forest and (c) Adaboost, as well as the most effective combination of relevant features among (1) library loan records, (2) book titles, (3) Nippon Decimal Classification...
To determine the most effective book recommendation method for libraries, we conducted a recommendation experiment using (1) collaborative filtering based on the library loan records, (2) association rule mining based on the same data, and (3) Amazon. The library loan records of a certain university library for the period 2006 to 2011 were used. We recommended books to 33 students and asked them to...
Application of data mining techniques in library data results interesting and useful patterns that can be used to improve services in university libraries. This paper presents results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset. Frequent sequential patterns containing book sequences borrowed by students are generated for minimum...
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