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- Title
Microblogging Hyperlink Recommendation with Tensor-based Clustering.
- Authors
Lei Wang; Dehong Gao; Chunlei Chen
- Abstract
Micro-blogging service, one of the most popular Web2.0 services, collects millions of user status every day. Due to the length limitation, users usually need to explore other ways to enrich the content of their micro-blogs. Some studies have provided findings to suggest that users can benefit from added hyperlinks in their micro-blogs. In this paper, we focus on the hyperlinks in micro-blogs and propose a new application, called hyperlink recommendation. We expect that the recommended hyperlinks can be used to enrich the information of user micro-blog. A three-order tensor is used to model the user-'micro-blog'-hyperlink relations. Two tensor-based clustering approaches, tensor decomposition-based clustering (TDC) and tensor approximation-based clustering (TAC) are applied to group the users, micro-blog and hyperlinks with similar interests, or similar use contexts. Recommendation is then made based on the reconstructed tensor using cluster information. The evaluation results in terms of Mean Absolute Error (MAE) shows the advantages of both the TDC and TAC approaches over a baseline recommendation approach, i.e., memory-based collaborative filtering. Comparatively, TAC approach achieves better performance than TDC approach.
- Subjects
MICROBLOGS; HYPERLINKS; DOCUMENT clustering
- Publication
International Journal of Simulation: Systems, Science & Technology, 2016, Vol 17, Issue 22, p20.1
- ISSN
1473-8031
- Publication type
Article
- DOI
10.5013/IJSSST.a.17.28.20