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Recommendation of newly published research papers using belief propagation
- Ha, Jiwoon;
- Kwon, Soon-Hyoung;
- Kim, Sang-Wook;
- Lee, Dongwon
Citations
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12초록
The problem to retrieve most relevant research papers for a given academic is studied. Existing solutions cannot adequately address the recommendation of new papers due to their lack of history information, the so-called cold start problem. Using the graphical model built from citation information between a new paper pi and published papers, toward this challenge, we propose a novel approach based on a probabilistic inference algorithm, the Belief Propagation (BP), to predict the likelihood of pi's relevance to a target academic. Compared to item-based collaborative filtering method using a DBLP data set, the empirical validation shows an improvement in accuracy up to 26% in F1 score.
키워드
Belief propagation; Data mining; Paper recommendation; Collaborative filtering; Data mining; Inference engines; Belief propagation; Citation information; Cold start problems; Empirical validation; History informations; Item-based collaborative filtering; Paper recommendations; Probabilistic inference; Paper
- 제목
- Recommendation of newly published research papers using belief propagation
- 저자
- Ha, Jiwoon; Kwon, Soon-Hyoung; Kim, Sang-Wook; Lee, Dongwon
- 발행일
- 2014-10
- 유형
- Conference Paper
- 저널명
- Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014
- 페이지
- 77 ~ 81