초록

용어보기논문에서 용어와 풀이말을 자동 추출한 결과로, 시범 서비스 중입니다. AI-Helper

Abstract ▼ AI-Helper

Today, researchers can access many different academic papers easily and Quickly through digital libraries but at the same time, researchers spend more time and effort to find relevant academic papers that they want. To alleviate these problems, personalized academic paper recommenders, which recomme...

Today, researchers can access many different academic papers easily and Quickly through digital libraries but at the same time, researchers spend more time and effort to find relevant academic papers that they want. To alleviate these problems, personalized academic paper recommenders, which recommend relevant academic papers to researchers considering researcher's interests have been proposed. Personalized academic paper recommenders using the content based filtering method have been actively studied because they properly recommend relevant text documents to users. However, those recommenders using the content based filtering method only consider the syntactic relation and exclude the semantic relation. To resolve this problem, we propose PARuS(Personalized Academic Paper Recommender using Semantic Relation in Digital Library Domain), which is a personalized academic paper recommender using the content based filtering method reflecting the latent semantic relations. PARuS constructs the researcher's preference feature information based on researcher's publication list, and then PARuS recommends relevant academic papers reflecting the syntactic relation and the latent semantic relation between researcher's preference feature information and candidate academic papers. In this paper, we evaluate the performance of PARuS and shows its improved effectiveness over previous method.

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