IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v452y2016icp192-208.html
   My bibliography  Save this article

Network-based recommendation algorithms: A review

Author

Listed:
  • Yu, Fei
  • Zeng, An
  • Gillard, Sébastien
  • Medo, Matúš

Abstract

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users’ past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use–such as the possible influence of recommendation on the evolution of systems that use it–and finally discuss open research directions and challenges.

Suggested Citation

  • Yu, Fei & Zeng, An & Gillard, Sébastien & Medo, Matúš, 2016. "Network-based recommendation algorithms: A review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 192-208.
    • Handle: RePEc:eee:phsmap:v:452:y:2016:i:c:p:192-208
    DOI: 10.1016/j.physa.2016.02.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116001874
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2016.02.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alexandre Vidmer & An Zeng & Mat'uv{s} Medo & Yi-Cheng Zhang, 2015. "Prediction in complex systems: the case of the international trade network," Papers 1511.05404, arXiv.org.
    2. Liu, Run-Ran & Liu, Jian-Guo & Jia, Chun-Xiao & Wang, Bing-Hong, 2010. "Personal recommendation via unequal resource allocation on bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3282-3289.
    3. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    4. Liu, Jian-Guo & Guo, Qiang & Zhang, Yi-Cheng, 2011. "Information filtering via weighted heat conduction algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2414-2420.
    5. Liu, Chuang & Zhou, Wei-Xing, 2012. "Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5704-5711.
    6. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    7. An Zeng & Stanislao Gualdi & Matúš Medo & Yi-Cheng Zhang, 2013. "Trend Prediction In Temporal Bipartite Networks: The Case Of Movielens, Netflix, And Digg," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(04n05), pages 1-15.
    8. Wei Zeng & An Zeng & Ming-Sheng Shang & Yi-Cheng Zhang, 2013. "Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    9. Zhu, Yu-Xiao & Lü, Linyuan & Zhang, Qian-Ming & Zhou, Tao, 2012. "Uncovering missing links with cold ends," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5769-5778.
    10. Jian-Guo Liu & Bing-Hong Wang & Qiang Guo, 2009. "Improved Collaborative Filtering Algorithm Via Information Transformation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 20(02), pages 285-293.
    11. Vidmer, Alexandre & Zeng, An & Medo, Matúš & Zhang, Yi-Cheng, 2015. "Prediction in complex systems: The case of the international trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 188-199.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhenghui Sha & Yun Huang & Jiawei Sophia Fu & Mingxian Wang & Yan Fu & Noshir Contractor & Wei Chen, 2018. "A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations," Complexity, Hindawi, vol. 2018, pages 1-14, January.
    2. Hu, Liang & Ren, Liang & Lin, Wenbin, 2018. "A reconsideration of negative ratings for network-based recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 690-701.
    3. Su, Zhan & Zheng, Xiliang & Ai, Jun & Shen, Yuming & Zhang, Xuanxiong, 2020. "Link prediction in recommender systems based on vector similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    4. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Ming-Yang Zhou & Yi-Cheng Zhang, 2018. "Enhancing Countries’ Fitness with Recommender Systems on the International Trade Network," Complexity, Hindawi, vol. 2018, pages 1-12, October.
    5. Ma, Wenping & Ren, Chen & Wu, Yue & Wang, Shanfeng & Feng, Xiang, 2017. "Personalized recommendation via unbalance full-connectivity inference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 273-279.
    6. Wang, Yang & Han, Lixin, 2020. "Personalized recommendation via network-based inference with time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    7. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Mingyang Zhou & Yi-Cheng Zhang, 2019. "Enhancing countries' fitness with recommender systems on the international trade network," Papers 1904.02412, arXiv.org.
    8. Zhu, Bei & Yeung, Chi Ho & Liem, Rhea Patricia, 2021. "The impact of common neighbor algorithm on individual friend choices and online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    9. Dong, Qiang & Yuan, Quan & Shi, Yang-Bo, 2019. "Alleviating the recommendation bias via rank aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    10. Chen, Ling-Jiao & Gao, Jian, 2018. "A trust-based recommendation method using network diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 679-691.
    11. Lee, Yan-Li & Zhou, Tao & Yang, Kexin & Du, Yajun & Pan, Liming, 2023. "Personalized recommender systems based on social relationships and historical behaviors," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    12. Zare, Hadi & Nikooie Pour, Mina Abd & Moradi, Parham, 2019. "Enhanced recommender system using predictive network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 322-337.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yichi Zhang & Zhiliang Dong & Sen Liu & Peixiang Jiang & Cuizhi Zhang & Chao Ding, 2021. "Forecast of International Trade of Lithium Carbonate Products in Importing Countries and Small-Scale Exporting Countries," Sustainability, MDPI, vol. 13(3), pages 1-23, January.
    2. Liu, Jin-Hu & Zhu, Yu-Xiao & Zhou, Tao, 2016. "Improving personalized link prediction by hybrid diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 199-207.
    3. Xuanru Zhou & Hua Zhang & Shuxian Zheng & Wanli Xing & Pei Zhao & Haiying Li, 2022. "The Crude Oil International Trade Competition Networks: Evolution Trends and Estimating Potential Competition Links," Energies, MDPI, vol. 15(7), pages 1-20, March.
    4. Feng, Sida & Li, Huajiao & Qi, Yabin & Guan, Qing & Wen, Shaobo, 2017. "Who will build new trade relations? Finding potential relations in international liquefied natural gas trade," Energy, Elsevier, vol. 141(C), pages 1226-1238.
    5. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Mingyang Zhou & Yi-Cheng Zhang, 2019. "Enhancing countries' fitness with recommender systems on the international trade network," Papers 1904.02412, arXiv.org.
    6. Liao, Hao & Zeng, An & Zhang, Yi-Cheng, 2015. "Predicting missing links via correlation between nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 216-223.
    7. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Ming-Yang Zhou & Yi-Cheng Zhang, 2018. "Enhancing Countries’ Fitness with Recommender Systems on the International Trade Network," Complexity, Hindawi, vol. 2018, pages 1-12, October.
    8. Liu, sen & Dong, Zhiliang, 2019. "Who will trade bauxite with whom? Finding potential links through link prediction," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    9. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    10. Chen, Ling-Jiao & Gao, Jian, 2018. "A trust-based recommendation method using network diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 679-691.
    11. Wen, Yuan & Liu, Yun & Zhang, Zhen-Jiang & Xiong, Fei & Cao, Wei, 2014. "Compare two community-based personalized information recommendation algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 199-209.
    12. Zhang, Xue & Wang, Xiaojie & Zhao, Chengli & Yi, Dongyun & Xie, Zheng, 2014. "Degree-corrected stochastic block models and reliability in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 553-559.
    13. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    14. Rafiee, Samira & Salavati, Chiman & Abdollahpouri, Alireza, 2020. "CNDP: Link prediction based on common neighbors degree penalization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    15. Wu, Rui-Jie & Shi, Gui-Yuan & Zhang, Yi-Cheng & Mariani, Manuel Sebastian, 2016. "The mathematics of non-linear metrics for nested networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 254-269.
    16. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    17. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    18. Moradabadi, Behnaz & Meybodi, Mohammad Reza, 2016. "Link prediction based on temporal similarity metrics using continuous action set learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 361-373.
    19. Cappelli, Federica & Carnazza, Giovanni & Vellucci, Pierluigi, 2023. "Crude oil, international trade and political stability: Do network relations matter?," Energy Policy, Elsevier, vol. 176(C).
    20. Kai Yang & Yuan Liu & Zijuan Zhao & Xingxing Zhou & Peijin Ding, 2023. "Graph attention network via node similarity for link prediction," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(3), pages 1-10, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:452:y:2016:i:c:p:192-208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.