Recommendation of research papers in DBpia: A Hybrid approach exploiting content and collaborative data
| Autor: | Yeon-Chang Lee, Jiwoon Ha, Kiburm Song, Jangho Yeo, Kichun Lee, Jungwan Yeom, Sang-Wook Kim |
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| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
Computer science 020204 information systems media_common.quotation_subject 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Context (language use) 02 engineering and technology Service provider Function (engineering) Hybrid approach media_common |
| Zdroj: | SMC |
| DOI: | 10.1109/smc.2016.7844691 |
| Popis: | DBpia is the largest digital-bibliography service provider in Korea. It provides several convenience functions for researchers. DBpia users (i.e., researchers) can search for papers via several search routes such as publications, publishers, authors, and keywords. Although the researchers can exploit the search functions, they may still have a number of search results as candidate papers to read. Therefore, it is crucial to provide a function of recommending most relevant papers to an individual user. In this paper, we (1) discuss several methods with four datasets of DBpia in the context of paper recommendation using content-based or graph-based recommendation, and (2) propose a hybrid approach suitable for paper recommendation combining the content-based and the graph-based approaches. We lastly conduct extensive experiments by a real-world academic literature dataset in DBpia to verify the effectiveness of our proposed approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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