TY  - JOUR
AB  - In a large‐scale digital library, it is essential to recommend a small number of useful and related articles to users. In this paper, a literature recommendation framework for digital libraries is proposed that dynamically provides recommendations to an active user when browsing a new article. This framework extends our previous work that considers only Web usage data by utilizing content information of articles when making recommendations. Methods that make use of pure content data, pure Web usage data, and both content and usage data are developed and compared using the data collected from our university's electronic thesis and dissertation (ETD) system. The experimental results demonstrate that content data and usage data are complements of each other and hybrid methods that take into account of both types of information tend to achieve more accurate recommendations.
VL  - 28
IS  - 4
SN  - 1468-4527
DO  - 10.1108/14684520410553750
UR  - https://doi.org/10.1108/14684520410553750
AU  - Hwang San‐Yih
AU  - Chuang Shi‐Min
PY  - 2004
Y1  - 2004/01/01
TI  - Combining article content and Web usage for literature recommendation in digital libraries
T2  - Online Information Review
PB  - Emerald Group Publishing Limited
SP  - 260
EP  - 272
Y2  - 2024/04/19
ER  -