The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network.
The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles.
The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method.
This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.
This work was partially supported in part by “Aim for the Top University Plan” of National Sun Yat-sen University in Taiwan and grants from Ministry of Science and Technology of Taiwan under Grant Nos MOST 104-2410-H-110 -039 -MY2 and MOST 104-2410-H-002-143-MY3.
Hwang, S.-Y., Wei, C.-P., Lee, C.-H. and Chen, Y.-S. (2017), "Coauthorship network-based literature recommendation with topic model", Online Information Review, Vol. 41 No. 3, pp. 318-336. https://doi.org/10.1108/OIR-06-2016-0166
Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited