Scientific Paper Recommendation: A Survey

被引:107
作者
Bai, Xiaomei [1 ]
Wang, Mengyang [2 ]
Lee, Ivan [3 ]
Yang, Zhuo [2 ]
Kong, Xiangjie [2 ]
Xia, Feng [2 ]
机构
[1] Anshan Normal Univ, Comp Ctr, Anshan 114007, Peoples R China
[2] Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China
[3] Univ South Australia, Sch Informat Technol & Math Sci, Adelaide, SA 5001, Australia
关键词
Recommender systems; scientific paper recommendation; recommendation algorithms; PERSONALIZED RECOMMENDATIONS; ARTICLE RECOMMENDATION; SYSTEM; CONNECTIONS; INFORMATION; ALGORITHM; DEAL; USER;
D O I
10.1109/ACCESS.2018.2890388
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Globally, the 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 the recommender systems are more effective and reliable than the 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 paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the 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 the scholarly paper recommendation.
引用
收藏
页码:9324 / 9339
页数:16
相关论文
共 112 条
  • [21] Tweet Can Be Fit: Integrating Data from Wearable Sensors and Multiple Social Networks for Wellness Profile Learning
    Farseev, Aleksandr
    Chua, Tat-Seng
    [J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2017, 35 (04)
  • [22] Personalized recommendations based on time-weighted overlapping community detection
    Feng, Haoyuan
    Tian, Jin
    Wang, Harry Jiannan
    Li, Minqiang
    [J]. INFORMATION & MANAGEMENT, 2015, 52 (07) : 789 - 800
  • [23] Feng Xia, 2016, IEEE Transactions on Big Data, V2, P101, DOI 10.1109/TBDATA.2016.2555318
  • [24] Ferrara F, 2011, COMM COM INF SC, V249, P14
  • [25] Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation
    Fouss, Francois
    Pirotte, Alain
    Renders, Jean-Michel
    Saerens, Marco
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2007, 19 (03) : 355 - 369
  • [26] GARFIELD E, 1995, SCIENTIST, V9, P11
  • [28] Gautam J., 2012, P WORLD C ENG, P1
  • [29] Gipp B., 2009, Proceedings of the International Conference on Emerging Trends in Computing, P309
  • [30] Research paper recommender systems: A random-walk based approach
    Gori, Marco
    Pucci, Augusto
    [J]. 2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, (WI 2006 MAIN CONFERENCE PROCEEDINGS), 2006, : 778 - +