Abstract:

By studying 82 papers published in the CNKI from 2003 to 2016 on collaborative filtering of Information
and Digital Library, we investigate the hottest topics of current and history on collaborative filtering of Information
and Digital Library. By classifying these papers, we discover that domestic researchers are engaged in either
propagating the collaborative filtering technique in academic databases or improving the algorithm, in which the
main problems are data sparseness and extensibility. By analyzing the papers further, we discover that researchers
prefer to combine the content-based recommend technique, custom data or different recommend results to solve
the data sparseness problem, and turn to the clustering technique to solve the problem of extensibility.