A Library Recommender System Using Interest Change over Time and Matrix Clustering

Abstract

AbstractA traditional library recommender system can not only employ individual history of library usage to recommend books which she (he) is interested, but also uses library usages of other users who are in the same social network to recommend the books which she (he) never loans but may be interested in. However, the same treatment for the user library usage at different times will lead to the recommended result departure from the users’ current information needs. Meanwhile, the data of library usage are highly dimensional and sparse. Thus, due to data sparsity and interest change over time, the traditional recommender systems cannot perform well. In order to tackle the two issues, this paper exploits time decay weight and matrix clustering to propose a novel library recommender system. Comparing two traditional recommender systems using K-Means clustering and hierarchical agglomerative clustering, experiments show promising results.