Skip to main content
SpringerLink
Log in

Collaborative filtering recommender systems taxonomy

  • Survey Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

In the era of internet access, recommender systems try to alleviate the difficulty that consumers face while trying to find items (e.g., services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of collaborative filtering recommend systems. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a collaborative filtering recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of collaborative filtering recommender systems according to their ability to efficiently handle well-known drawbacks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Movie Lens Datasets available at: http://grouplens.org/datasets/movielens/1m/.

  2. Pinterest Dataset available at: https://sites.google.com/site/xueatalphabeta/academic-projects.

  3. Yelp Dataset available at: https://github.com/hexiangnan/sigir16-eals.

  4. Gowalla dataset available at: http://dawenl.github.io/data/gowalla pro.zip.

  5. Million song dataset available at: https://labrosa.ee.columbia.edu/millionsong/.

  6. Yahoo! Webscope R4 dataset available at: http://webscope.sandbox.yahoo.com.

References

  1. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749. https://doi.org/10.1109/TKDE.2005.99

    Article  Google Scholar 

  2. Agarwal D, Chen BC (2009) Regression-based latent factor models. In: 15th ACM SIGKDD international conference on knowledge discovery and data mining. KDD ’09. ACM, New York, pp 19–28

  3. Aggarwal CC (2016) Recommender systems: the textbook, 1st edn. Springer, Berlin

  4. Aggarwal CC et al (2016) Recommender systems. Springer, Berlin

    Book  Google Scholar 

  5. Aggarwal CC, Gates SC, Yu PS (2004) On using partial supervision for text categorization. IEEE Trans Knowl Data Eng 16(2):245–255. https://doi.org/10.1109/TKDE.2004.1269601

    Article  Google Scholar 

  6. Al-bashiri H, Abdulgabber MA, Romli A, Kahtan H (2018) An improved memory-based collaborative filtering method based on the topsis technique. PLoS ONE 13(10):1–26. https://doi.org/10.1371/journal.pone.0204434

    Article  Google Scholar 

  7. Alhijawi B (2017) The use of the genetic algorithms in the recommender systems. Ph.D. thesis, Hashemite University

  8. Al-Shamri MYH, Bharadwaj KK (2008) Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Syst Appl 35(3):1386–1399

    Article  Google Scholar 

  9. Amatriain X, Jaimes A, Oliver N, Pujol JM (2011) Data mining methods for recommender systems. In: Recommender systems handbook. Springer, Berlin, pp 39–71

  10. Bag S, Ghadge A, Tiwari MK (2019) An integrated recommender system for improved accuracy and aggregate diversity. Comput Ind Eng 130:187–197. https://doi.org/10.1016/j.cie.2019.02.028

  11. Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72. https://doi.org/10.1145/245108.245124

    Article  Google Scholar 

  12. Batmaz Z, Yurekli A, Bilge A, Kaleli C (2019) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52(1):1–37

    Article  Google Scholar 

  13. Bennett J, Lanning S, et al (2007) The netflix prize. In: Proceedings of KDD cup and workshop, vol. 2007, p. 35. New York, NY, USA

  14. Berbague C, Karabadji NE, Seridi H (2018) Recommendation diversification using a weighted similarity measure in user based collaborative filtering. In: 2018 International symposium on programming and systems (ISPS), pp 1–6. https://doi.org/10.1109/ISPS.2018.8379011

  15. Billsus D, Pazzani MJ (1998) Learning collaborative information filters. In: Proceedings of the fifteenth international conference on machine learning, ICML ’98. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 46–54

  16. Bobadilla J, Ortega F, Hernando A, Alcalá J (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl Based Syst 24:1310–1316

    Article  Google Scholar 

  17. Bobadilla J, Ortega F, Hernando A, GutiéRrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132

    Article  Google Scholar 

  18. Bojnordi E, Moradi P (2012) A novel collaborative filtering model based on combination of correlation method with matrix completion technique. In: Artificial intelligence and signal processing (AISP), 2012 16th CSI international symposium on, pp 191–194. IEEE

  19. Bourke S (2015) The application of recommender systems in a multi site, multi domain environment. In: Proceedings of the 9th ACM conference on recommender systems, RecSys ’15, p. 229. Association for Computing Machinery, New York. https://doi.org/10.1145/2792838.2799495

  20. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117. https://doi.org/10.1016/S0169-7552(98)00110-X

    Article  Google Scholar 

  21. Candes EJ, Plan Y (2010) Matrix completion with noise. Proc IEEE 98(6):925–936

    Article  Google Scholar 

  22. Chae DK, Lee SC, Lee SY, Kim SW (2018) On identifying k-nearest neighbors in neighborhood models for efficient and effective collaborative filtering. Neurocomputing 278:134–143. https://doi.org/10.1016/j.neucom.2017.06.081

  23. Chiluka N, Andrade N, Pouwelse J (2011) A link prediction approach to recommendations in large-scale user-generated content systems. In: Proceedings of the 33rd European conference on advances in information retrieval, ECIR’11, pp 189–200. Springer, Berlin. http://dl.acm.org/citation.cfm?id=1996889.1996914

  24. Comon P, Luciani X, De Almeida AL (2009) Tensor decompositions, alternating least squares and other tales. J Chemometrics: J Chemometrics Soc 23(7–8):393–405

    Article  Google Scholar 

  25. Da’u A, Salim N (2019) Recommendation system based on deep learning methods: a systematic review and new directions. Artif Intell Rev, pp 1–40. https://doi.org/10.1007/s10462-019-09744-1

  26. Deng S, Huang L, Xu G, Wu X, Wu Z (2017) On deep learning for trust-aware recommendations in social networks. IEEE Trans Neural Netw Learn Syst 28(5):1164–1177. https://doi.org/10.1109/TNNLS.2016.2514368

    Article  Google Scholar 

  27. Deshpande M, Karypis G (2004) Selective markov models for predicting web page accesses. ACM Trans Internet Technol 4(2):163–184

    Article  Google Scholar 

  28. dos Santos C, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 69–78

  29. Ebesu T, Fang Y (2017) Neural citation network for context-aware citation recommendation. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 1093–1096. ACM

  30. Ekstrand MD, Riedl JT, Konstan JA (2011) Collaborative filtering recommender systems. Found Trends Hum-Comput Interact 4(2):81–173. https://doi.org/10.1561/1100000009

    Article  Google Scholar 

  31. Ge X, Liu J, Qi Q, Chen Z (2011) A new prediction approach based on linear regression for collaborative filtering. In: International conference on fuzzy systems and knowledge discovery, pp 2586–2590. IEEE

  32. Han S, Mao H, Dally WJ (2015) Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149

  33. Haveliwala TH (2003) Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans Knowl Data Eng 15(4):784–796. https://doi.org/10.1109/TKDE.2003.1208999

    Article  Google Scholar 

  34. Haveliwala T, Kamvar S, Jeh G (2003) An analytical comparison of approaches to personalizing pagerank. Technical Report 2003-35, Stanford InfoLab. http://ilpubs.stanford.edu:8090/596/

  35. He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 355–364. ACM

  36. He X, Du X, Wang X, Tian F, Tang J, Chua TS (2018) Outer product-based neural collaborative filtering. arXiv preprint arXiv:1808.03912

  37. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 173–182

  38. Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’99, pp 230–237. ACM, New York. https://doi.org/10.1145/312624.312682

  39. Herlocker J, Konstan JA, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retrieval 5(4):287–310. https://doi.org/10.1023/A:1020443909834

    Article  Google Scholar 

  40. Hernando A, Bobadilla J, Ortega F (2016) A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowl-Based Syst 97:188–202

    Article  Google Scholar 

  41. Hinton G, Deng L, Yu D, Dahl GE, Mohamed Ar, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Article  Google Scholar 

  42. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366

    Article  Google Scholar 

  43. Hu Y, Shi W, Li H, Hu X (2017) Mitigating data sparsity using similarity reinforcement-enhanced collaborative filtering. ACM Trans Internet Technol 17(3):3:11-3:120. https://doi.org/10.1145/3062179

    Article  Google Scholar 

  44. Huang Z, Li X, Chen H (2005) Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS joint conference on digital libraries (JCDL ’05), pp 141–142

  45. Iaquinta L, De Gemmis M, Lops P, Semeraro G, Filannino M, Molino P (2008) Introducing serendipity in a content-based recommender system. In: 2008 Eighth international conference on hybrid intelligent systems, pp 168–173

  46. Isinkaye F, Folajimi Y, Ojokoh B (2015) Recommendation systems: principles, methods and evaluation. Egypt Inform J 16(3):261–273

    Article  Google Scholar 

  47. Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recognit Lett 31(8):651–666

    Article  Google Scholar 

  48. Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’09, pp 397–406. ACM, New York. https://doi.org/10.1145/1557019.1557067

  49. Jannach D, Resnick P, Tuzhilin A, Zanker M (2016) Recommender systems-beyond matrix completion. Commun ACM 59(11):94–102

    Article  Google Scholar 

  50. Jeh G, Widom J (2002) Simrank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’02, pp 538–543. ACM, New York. https://doi.org/10.1145/775047.775126

  51. Jiang J, Li W, Dong A, Gou Q, Luo X (2020) A fast deep autoencoder for high-dimensional and sparse matrices in recommender systems. Neurocomputing 412:381–391

    Article  Google Scholar 

  52. Kant V, Bharadwaj KK (2013) Integrating collaborative and reclusive methods for effective recommendations: a fuzzy Bayesian approach. Int J Intell Syst 28(11):1099–1123. https://doi.org/10.1002/int.21619

    Article  Google Scholar 

  53. Khatri M (2012) A survey of naïve bayesian algorithms for similarity in recommendation systems. Int J Adv Res Comput Sci Softw Eng 2(5)

  54. Khusro S, Ali Z, Ullah I (2016) Recommender systems: issues, challenges, and research opportunities. Springer, Singapore, pp 1179–1189

  55. Khusro S, Ali Z, Ullah I (2016) Recommender systems: issues, challenges, and research opportunities. Springer, Singapore, pp 1179–1189. https://doi.org/10.1007/978-981-10-0557-2_112

  56. Kim Kj, Ahn H (2005) Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems. In: Kim TG (ed) Artificial intelligence and simulation. Springer, Berlin, pp 409–415

  57. Koohi H, Kiani K (2017) A new method to find neighbor users that improves the performance of collaborative filtering. Expert Syst Appl 83(C):30–39. https://doi.org/10.1016/j.eswa.2017.04.027

    Article  Google Scholar 

  58. Koohi H, Kiani K (2016) User based collaborative filtering using fuzzy c-means. Measurement 91:134–139. https://doi.org/10.1016/j.measurement.2016.05.058

  59. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 8:30–37

    Article  Google Scholar 

  60. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  61. Kumar B, Sharma N (2016) Approaches, issues and challenges in recommender systems: a systematic review. Indian J Sci Technol 9(47). http://www.indjst.org/index.php/indjst/article/view/94892

  62. Leung CWK, Chan SCF, Chung Fl (2006) A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10(3), 357–381. https://doi.org/10.1007/s10115-006-0002-1

  63. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inform Sci Technol 58(7):1019–1031. https://doi.org/10.1002/asi.20591

    Article  Google Scholar 

  64. Linden G, Smith B, York J (2003) Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput 1:76–80

    Article  Google Scholar 

  65. Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl-Based Syst 56:156–166. https://doi.org/10.1016/j.knosys.2013.11.006

  66. Liu X, Ouyang Y, Rong W, Xiong Z (2015) Item category aware conditional restricted Boltzmann machine based recommendation. In: International conference on neural information processing. Springer, pp 609–616

  67. Li J, Zhang L, Meng F, Li F (2014) Recommendation algorithm based on link prediction and domain knowledge in retail transactions. Procedia Comput Sci 31:875–881. https://doi.org/10.1016/j.procs.2014.05.339

  68. Mild A, Natter M (2002) Collaborative filtering or regression models for internet recommendation systems? J Target Meas Anal Mark 10(4):304–313

    Article  Google Scholar 

  69. Moradi P, Ahmadian S, Akhlaghian F (2015) An effective trust-based recommendation method using a novel graph clustering algorithm. Physica A 436:462–481

    Article  Google Scholar 

  70. Nguyen TT, Hui PM, Harper FM, Terveen L, Konstan JA (2014) Exploring the filter bubble: the effect of using recommender systems on content diversity. In: Proceedings of the 23rd international conference on world wide web, WWW ’14. ACM, New York, pp 677–686

  71. Nilashi M, Ibrahim O, Bagherifard K (2018) A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst Appl 92:507–520

    Article  Google Scholar 

  72. Page L, Brin S, Motwani R, Winograd T (1998) The pagerank citation ranking: bringing order to the web. In: Proceedings of the 7th international world wide web conference. Brisbane, Australia, pp 161–172. https://www.citeseer.nj.nec.com/page98pagerank.html

  73. Panagiotakis C (2015) Point clustering via voting maximization. J Classif 32(2):212–240

    Article  MathSciNet  Google Scholar 

  74. Panagiotakis C, Papadakis H, Grinias E, Komodakis N, Fragopoulou P, Tziritas G (2013) Interactive image segmentation based on synthetic graph coordinates. Pattern Recognit 46(11):2940–2952

    Article  Google Scholar 

  75. Panagiotakis C, Papadakis H, Fragopoulou P (2018) Detection of hurriedly created abnormal profiles in recommender systems. In: International conference on intelligent systems

  76. Panagiotakis C, Papadakis H, Fragopoulou P (2020) A user training error based correction approach combined with the synthetic coordinate recommender system. In: International conference on user modeling, adaptation and personalization

  77. Panagiotakis C, Papadakis H, Fragopoulou P (2020) Personalized video summarization based exclusively on user preferences. In: European conference on information retrieval

  78. Panagiotakis C, Papadakis H, Fragopoulou P (2020) Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems. Mach Learn Cybern

  79. Papadakis H, Panagiotakis C, Fragopoulou P (2014) Distributed detection of communities in complex networks using synthetic coordinates. J Stat Mech: Theory Exp 2014(3):P03013

    Article  Google Scholar 

  80. Papadakis H, Panagiotakis C, Fragopoulou P (2017) Scor: a synthetic coordinate based recommender system. Expert Syst Appl 79:8–19

    Article  Google Scholar 

  81. Park ST, Chu W (2009) Pairwise preference regression for cold-start recommendation. In: RecSys, pp 21–28

  82. Perera D, Zimmermann R (2018) Lstm networks for online cross-network recommendations. In: IJCAI, pp 3825–3833

  83. Ramezani M, Moradi P, Akhlaghian F (2014) A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains. Phys A: Stat Mech Appl 408:72–84. https://doi.org/10.1016/j.physa.2014.04.002

  84. Ray S (2015) 7 types of regression techniques you should know! www.analyticsvidhya.com . https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/

  85. Rencher ACW (2012) Methods of multivariate analysis. In: Wiley series in probability and statistics, chap. 10.1. Wiley, London

  86. Rendle S (2012) Factorization machines with libfm. ACM Trans Intell Syst Technol 3(3):1–22

    Article  Google Scholar 

  87. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on Machine learning, pp 791–798. ACM

  88. Salehi M, Pourzaferani M, Razavi SA (2013) Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egypt Inform J 14(1):67–78

    Article  Google Scholar 

  89. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295. ACM

  90. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, WWW ’01, pp 285–295. ACM, New York. https://doi.org/10.1145/371920.372071

  91. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, WWW ’01, pp. 285–295. ACM, New York

  92. Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: Järvelin K, Beaulieu M, Baeza-Yates RA, Myaeng S (eds) SIGIR 2002: proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, August 11–15, Tampere, Finland, pp 253–260. ACM. https://doi.org/10.1145/564376.564421

  93. Shahabi C, Chen YS (2003) Web information personalization: challenges and approaches. In: Bianchi-Berthouze N (ed) Databases in networked information systems. Springer, Berlin, pp 5–15

    Chapter  Google Scholar 

  94. Shah L, Hetal G, Prem B (2016) Survey on recommendation system. System 137(7)

  95. Shani G, Heckerman D, Brafman RI (2005) An mdp-based recommender system. J Mach Learn Res 6:1265–1295

    MathSciNet  MATH  Google Scholar 

  96. Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’95, pp 210–217. ACM Press/Addison-Wesley Publishing Co., New York, NY, USA. https://doi.org/10.1145/223904.223931

  97. Sharif MA, Raghavan VV (2017) Link prediction based hybrid recommendation system using user-page preference graphs. In: Proceedings of the 2017 international joint conference on neural networks (IJCNN) . https://doi.org/10.1109/IJCNN.2017.7965981

  98. Sharma L, Gera A (2013) A survey of recommendation system research challenges. Int J Eng Trends Technol

  99. Sharma L, Gera A (2013) A survey of recommendation system: research challenges. Int J Eng Trends Technol

  100. Shih HS, Shyur HJ, Lee ES (2007) An extension of topsis for group decision making. Math Comput Model 45(7):801–813. https://doi.org/10.1016/j.mcm.2006.03.023

  101. Singh S, Bag S, Jenamani M (2015) Relative similarity based approach for improving aggregate recommendation diversity. In: 2015 Annual IEEE India conference (INDICON), pp 1–6. https://doi.org/10.1109/INDICON.2015.7443856

  102. Smirnov A, Ponomarev A, Kashevnik A (2017) Multi-model service for recommending tourist attractions. In: Hammoudi S, Maciaszek LA, Missikoff MM, Camp O, Cordeiro J (eds) Enterprise information systems. Springer, Cham, pp 364–386

    Chapter  Google Scholar 

  103. Son LH (2014) Hu-fcf: a hybrid user-based fuzzy collaborative filtering method in recommender systems. Expert Syst Appl 41(15):6861–6870. https://doi.org/10.1016/j.eswa.2014.05.001

  104. Sorzano COS, Vargas J, Montano AP (2014) A survey of dimensionality reduction techniques. arXiv preprint arXiv:1403.2877

  105. Strub F, Mary J, Gaudel R (2016) Hybrid collaborative filtering with autoencoders. arXiv preprint arXiv:1603.00806

  106. Suzuki Y, Ozaki T (2017) Stacked denoising autoencoder-based deep collaborative filtering using the change of similarity. In: 2017 31st International conference on advanced information networking and applications workshops (WAINA), pp 498–502. IEEE

  107. Tsai MH, Aggarwal C, Huang T (2014) Ranking in heterogeneous social media. In: Proceedings of the 7th ACM international conference on web search and data mining, WSDM ’14. ACM, New York, pp 613–622. https://doi.org/10.1145/2556195.2556254

  108. Tsai CF, Hung C (2012) Cluster ensembles in collaborative filtering recommendation. Appl Soft Comput 12(4):1417–1425

    Article  Google Scholar 

  109. Van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Advances in neural information processing systems, pp 2643–2651

  110. Vargas S, Castells P (2011) Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the fifth ACM conference on recommender systems, RecSys ’11. ACM, New York, pp 109–116. https://doi.org/10.1145/2043932.2043955

  111. Vucetic S, Obradovic Z (2005) Collaborative filtering using a regression-based approach. Knowl Inf Syst 7:1–22

    Article  Google Scholar 

  112. Wang D, Zhang X, Yu D, Xu G, Deng S (2021) Came: content- and context-aware music embedding for recommendation. IEEE Trans Neural Netw Learn Syst 32(3):1375–1388. https://doi.org/10.1109/TNNLS.2020.2984665

    Article  Google Scholar 

  113. Wu X, Cheng B, Chen J (2017) Collaborative filtering service recommendation based on a novel similarity computation method. IEEE Trans Serv Comput 10(3):352–365. https://doi.org/10.1109/TSC.2015.2479228

    Article  Google Scholar 

  114. Wu X, Huang Y (2017) Sigra: a new similarity computation method in recommendation system. In: 2017 International conference on cyber-enabled distributed computing and knowledge discovery (CyberC), pp 148–154. https://doi.org/10.1109/CyberC.2017.89

  115. Wu S, Ren W, Yu C, Chen G, Zhang D, Zhu J (2016) Personal recommendation using deep recurrent neural networks in netease. In: Data Engineering (ICDE), 2016 IEEE 32nd international conference on, pp 1218–1229. IEEE

  116. Xie F, Chen Z, Shang J, Feng X, Li J (2015) A link prediction approach for item recommendation with complex number. Knowl-Based Syst 81:148–158. https://doi.org/10.1016/j.knosys.2015.02.013

  117. Xie W, Ouyang Y, Ouyang J, Rong W, Xiong Z (2016) User occupation aware conditional restricted boltzmann machine based recommendation. In: Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016 IEEE international conference on, pp 454–461. IEEE

  118. Xi D, Zhuang F, Song B, Zhu Y, Chen S, Hong D, Chen T, Gu X, He Q (2020) Neural hierarchical factorization machines for user’s event sequence analysis. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 1893–1896

  119. Xue GR, Lin C, Yang Q, Xi W, Zeng HJ, Yu Y, Chen Z (2005) Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp 114–121. ACM

  120. Zenebea A, Norciob AF (2003) Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst 160:76–94

    Article  MathSciNet  Google Scholar 

  121. Zhang Y, Koren J (2007) Efficient bayesian hierarchical user modeling for recommendation system. In: International ACM SIGIR conference on research and development in information retrieval

  122. Zhang Z, Lin H, Liu K, Wu D, Zhang G, Lu J (2013) A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf Sci 235:117–129. https://doi.org/10.1016/j.ins.2013.01.025

  123. Zhang Z, Robinson D, Tepper J (2018) Detecting hate speech on twitter using a convolution-gru based deep neural network. In: European semantic web conference, pp 745–760. Springer

  124. Zhang Q, Wang J, Huang H, Huang X, Gong Y (2017) Hashtag recommendation for multimodal microblog using co-attention network. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, IJCAI 2017, Melbourne, Australia, pp 3420–3426

  125. Zhang S, Yao L, Sun A (2017) Deep learning based recommender system: a survey and new perspectives. arXiv preprint arXiv:1707.07435

  126. Zheng Z, Ma H, Lyu MR, King I (2011) Qos-aware web service recommendation by collaborative filtering. IEEE Trans Serv Comput 4(2):140–152. https://doi.org/10.1109/TSC.2010.52

    Article  Google Scholar 

  127. Zheng Z, Ma H, Lyu MR, King I (2009) Wsrec: a collaborative filtering based web service recommender system. In: 2009 IEEE international conference on web services, pp 437–444. https://doi.org/10.1109/ICWS.2009.30

Download references

Acknowledgements

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK-02147).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004, Heraklion, Crete, Greece

    Harris Papadakis, Antonis Papagrigoriou, Eleftherios Kosmas & Paraskevi Fragopoulou

  2. Department of Management Science and Technology, Hellenic Mediterranean University, 72100, Agios Nikolaos, Crete, Greece

    Costas Panagiotakis

  3. Foundation for Research and Technology-Hellas (FORTH), Institute of Computer Science, 70013, Heraklion, Crete, Greece

    Costas Panagiotakis & Paraskevi Fragopoulou

Authors
  1. Harris Papadakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonis Papagrigoriou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Costas Panagiotakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eleftherios Kosmas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Paraskevi Fragopoulou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harris Papadakis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Papadakis, H., Papagrigoriou, A., Panagiotakis, C. et al. Collaborative filtering recommender systems taxonomy. Knowl Inf Syst 64, 35–74 (2022). https://doi.org/10.1007/s10115-021-01628-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-021-01628-7

Keywords

Search

Navigation