The research on personalized recommendation algorithm of library based on big data and association rules

Abstract

In order to provide better information consistent with their preferences features for users, personalized recommendation technology has become an important research field of digital libraries and get more and more attention from searchers. Among them, the large data mining and association rules-based personalized recommendation technology is the focus of research in the field of recommendation. In this paper, these two issues are studied. In order to increase the lending rate of collections, this paper use association rules analyzes for borrowing pattern mining, to obtain library users interests, to analyze different types of readers’ purpose library collections, and automatically provide readers with other books related to such book. Through improved frequent pattern growth algorithm, combined with online recommended and offline recommendation method, achieved a more satisfactory recommendation results. Finally, taken experimental analysis and verification for these techniques studies, and future research were discussed.