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- W3082397718 abstract "Globally, recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields including economic, education, and scientific research. Different empirical studies have shown that recommender systems are more effective and reliable than keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this survey, we first introduce the importance and advantages of paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-Based methods, Collaborative Filtering methods, Graph-Based methods and Hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on scholarly paper recommendation." @default.
- W3082397718 created "2020-09-08" @default.
- W3082397718 creator A5030323127 @default.
- W3082397718 creator A5044388284 @default.
- W3082397718 creator A5046174953 @default.
- W3082397718 creator A5067502218 @default.
- W3082397718 creator A5086712669 @default.
- W3082397718 creator A5089615958 @default.
- W3082397718 date "2020-08-09" @default.
- W3082397718 modified "2023-09-23" @default.
- W3082397718 title "Scientific Paper Recommendation: A Survey" @default.
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