Original scientific paper

https://doi.org/10.17559/TV-20160602011232

A PageRank-based collaborative filtering recommendation approach in digital libraries

Shanshan Guo orcid.org/0000-0003-3937-5769 ; Library, Zhejiang University of Finance and Economics, 18 Xueyuan Street, Xiasha, Hangzhou, China 310018
Wenyu Zhang orcid.org/0000-0002-8906-5411 ; Zhejiang University of Finance and Economics, 18 Xueyuan Street, Xiasha, Hangzhou, China 310018
Shuai Zhang ; Zhejiang University of Finance and Economics, 18 Xueyuan Street, Xiasha, Hangzhou, China 310018

Abstract

In the current era of big data, the explosive growth of digital resources in Digital Libraries (DLs) has led to the serious information overload problem. This trend demands personalized recommendation approaches to provide DL users with digital resources specific to their individual needs. In this paper we present a personalized digital resource recommendation approach, which combines PageRank and Collaborative Filtering (CF) techniques in a unified framework for recommending right digital resources to an active user by generating and analyzing a time-aware network of both user relationships and resource relationships from historical usage data. To address the existing issues in DL deployment, including unstable user profiles, unstable digital resource features, data sparsity and cold start problem, this work adapts the personalized PageRank algorithm to rank the time-aware resource importance for more effective CF, by searching for associative links connecting both active user and his/her initially preferred resources. We further evaluate the performance of the proposed methodology through a case study relative to the traditional CF technique operating on the same historical usage data from a DL.

Keywords

collaborative filtering; digital library; PageRank algorithm; recommendation approach; social network

Hrčak ID:

185443

URI

https://hrcak.srce.hr/185443

Publication date:

31.7.2017.

Article data in other languages: croatian

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