Abstract
With the tremendous amount of research publications, it has become increasingly important to provide a researcher with a rapid and accurate recommendation of a list of reference papers about a research field or topic. In this paper, we propose a unified graph model that can easily incorporate various types of useful information (e.g., content, authorship, citation and collaboration networks etc.) for efficient recommendation. The proposed model not only allows to thoroughly explore how these types of information can be better combined, but also makes personalized query-oriented reference paper recommendation possible, which as far as we know is a new issue that has not been explicitly addressed in the past. The experiments have demonstrated the clear advantages of personalized recommendation over non-personalized recommendation.
| Original language | English |
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| Title of host publication | CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management |
| Pages | 1509-1512 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 11 Dec 2013 |
| Event | 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States Duration: 27 Oct 2013 → 1 Nov 2013 |
Conference
| Conference | 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 |
|---|---|
| Country/Territory | United States |
| City | San Francisco, CA |
| Period | 27/10/13 → 1/11/13 |
Keywords
- A unified graph-based recommendation model
- Personalized reference paper recommendation
ASJC Scopus subject areas
- Decision Sciences(all)
- Business, Management and Accounting(all)