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Application of an Improved Collaborative Filtering Method on Recommending Books in College Libraries
Received date: 2016-10-08
Revised date: 2016-12-09
Online published: 2016-12-20
[Purpose/significance] In view of the problems of "sparse data" and "cold start" in the process of book recommendation in the colleges or universities, optimizing the readers' evaluation matrix and the similarity model is the key to improve the quality of book recommendation.[Method/process] An improved collaborative filtering method is proposed based on the classification of books to generate user evaluation matrix, and optimizes sample data in the matrix by considering the influence of the lending type, time and the similarities of the book on the user's interest. In addition, characteristics of readers and books are taken into consideration in calculating the reader's similarity, which can effectively solve the the problems of "sparse data" and "cold start" and significantly reduce the amount of computation.[Result/conclusion] The experimental results show that the improved method is better than the basic collaborative filtering and clustering collaborative filtering method, and it has a great improvement on the recommendation accuracy and user satisfaction, thus the effect of integrated recommendation is better.
Song Chuping . Application of an Improved Collaborative Filtering Method on Recommending Books in College Libraries[J]. Library and Information Service, 2016 , 60(24) : 86 -91 . DOI: 10.13266/j.issn.0252-3116.2016.24.012
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