Skip to main content
SpringerLink
Log in

A hybrid semantic recommender system enriched with an imputation method

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recommender systems are widely used in many applications. They can be viewed as the predictor systems that are to suggest accurate and highly preferred items to consumers or clients. These systems can be considered to be information filtering systems. They counter some important challenges such as cold start (it means the absence of enough data for a new item to make accurate recommendations), scalability, and sparsity. The memory-based recommender systems have high accuracy but lack scalability. Also, the model-based systems are scalable but not accurate. Current recommender systems use hybrid methods to deal with the most important shortages of traditional filtering approaches. Current recommender systems are usually a hybrid of content-based filtering and collaborative filtering, and so on. In this paper, a hybrid recommender system is presented to meet the stated challenges, increase system performance and provide more accurate recommendations. This system uses both content-based filtering and collaborative filtering. In addition, using an automatically collected wordnet, we create an ontology that has been used in the content-based filtering section of our proposed approach. Furthermore, this framework applies KNN (k nearest neighbors) algorithm and clustering to improve its functionality. The proposed system is evaluated on a real benchmark. The experimentations show the proposed method has a better performance compared with the current superior related methods. The experimentations also show that our recommender system has desirable scalability compared with the state-of-the-art recommender systems.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

The sample dataset can be accessed at movielens data center.

References

  1. Abdelwahab A, Sekiya H, Matsuba I, Horiuchi Y, Kuroiwa S, Nishida M (2009) An efficient collaborative filtering algorithm using SVD-free latent semantic indexing and particle swarm optimization. In: 2009 International Conference on Natural Language Processing and Knowledge Engineering, pp 1–4. https://doi.org/10.1109/NLPKE.2009.5313754

    Chapter  Google Scholar 

  2. Adomavicius G, Kwon Y (2007) New recommendation techniques for multicriteria rating systems. IEEE Intell Syst 22(3). https://doi.org/10.1109/MIS.2007.58

  3. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Knowl Data Eng IEEE Trans 17(6):734–749

    Google Scholar 

  4. Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor P (eds) Recommender systems handbook. Springer, Boston. https://doi.org/10.1007/978-0-387-85820-3_7

    Chapter  Google Scholar 

  5. Adomavicius G, Bauman K, Mobasher B, Ricci F, Tuzhilin A, Unger M (2020) Workshop on context-aware recommender systems. In: Santos RLT, Marinho LB, Daly EM, Chen L, Falk K, Koenigstein N, de Moura ES (eds) RecSys 2020: fourteenth ACM conference on recommender systems, virtual event, Brazil, September 22–26, 2020. ACM, New York, pp 635–637

    Google Scholar 

  6. Aha DW, Goldstone RL (1992) Concept learning and flexible weighting. In: Proceedings of the 14th annual conference of the cognitive science society. Erlbaum, Hillsdale, pp 534–539

    Google Scholar 

  7. Al-Shamri MYH (2016) User profiling approaches for demographic recommender systems. Knowl-Based Syst 100:175–187. https://doi.org/10.1016/j.knosys.2016.03.006]

    Article  Google Scholar 

  8. Anand SS, Mobasher B (2003) Intelligent techniques for web personalization. In: Proceedings of the 2003 international conference on intelligent techniques for web personalization, pp 1–36. https://doi.org/10.1007/11577935_1

    Chapter  Google Scholar 

  9. Anand SS, Kearney P, Shapcott M (2007) Generating semantically enriched user profiles for web personalization. ACM Trans Int Technol (TOIT) 7(4):22

    Google Scholar 

  10. Armstrong JS (2001) Principles of forecasting: a handbook for researchers and practitioners, vol 30. Springer Science & Business Media. https://doi.org/10.1007/978-0-306-47630-3

    Book  Google Scholar 

  11. Bagherifard K, Rahmani M, Nilashi M, Rafe V (2017) Performance improvement for recommender systems using ontology. Telematics Informatics 34(8):1772–1792. https://doi.org/10.1016/j.tele.2017.08.008

    Article  Google Scholar 

  12. Bahrani P, Minaei-Bidgoli B, Parvin H, Mirzarezaee M, Keshavarz A, Alinejad-Rokny H (2020) User and item profile expansion for dealing with cold start problem. J Intell Fuzzy Syst 38(4):4471–4483

    Google Scholar 

  13. Bambini R, Cremonesi P, Turrin R (2011) A recommender system for an IPTV service provider: a real large-scale production environment. In: Recommender systems handbook. Springer, pp 299–331

  14. Bobadilla A, Hernando FO, Bernal J (2011) A framework for collaborative filtering recommender systems. Expert Syst Appl 38:14609–14623. https://doi.org/10.1016/j.eswa.2011.05.021

    Article  Google Scholar 

  15. Bennett J, Lanning S (2007) The netflix prize, In KDD Cup and Workshop in Conjunction with KDD

  16. Bilgic M, Mooney R (2005) Explaining Recommendations: Satisfaction vs. Promotion. Conference: Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces

  17. Bobadilla J, Hernando A, Ortega F, Bernal J (2011) A framework for collaborative filtering recommender systems. Expert Syst Appl 38(12):14609–14623

    Google Scholar 

  18. Bobadilla J, Ortega F, Hernando A, deRivera GG (2013a) A similarity metric designed to speed up, using hardware, the recommender systems k-nearest neighbors algorithm. Knowl-Based Syst 51:27–34

    Google Scholar 

  19. Bobadilla J, Ortega F, Hernando A, Gutierrez A (2013b) Recommend Syst Surv Knowl Based Syst 46(0):109–132

    Google Scholar 

  20. Bokde D, Girase S, Mukhopadhyay D (2015) Matrix factorization model in collaborative filtering algorithms: a survey. Procedia Comput Sci 49:136–146. https://doi.org/10.1016/j.procs.2015.04.237

    Article  Google Scholar 

  21. Breese, JS, Heckerman, D, Kadie, C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Paper presented at the Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

  22. Buitelaar, P, Cimiano, P, Magnini, B (2005) Ontology learning from text: methods, evaluation and applications (Vol. 123): IOS press

  23. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12:331–370. https://doi.org/10.1023/A:1021240730564

    Article  Google Scholar 

  24. Burke, R (2007a) Hybrid web recommender systems the adaptive web (pp. 377–408): Springer

  25. Burke R (2007) Hybrid Web Recommender Systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web. Lecture notes in computer science, vol 4321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72079-9_12

    Chapter  Google Scholar 

  26. Chen S, Peng Y (2018) Matrix factorization for recommendation with explicit and implicit feedback. Knowl-Based Syst 158:109–117

    Google Scholar 

  27. Cheng L-C, Wang H-A (2014) A fuzzy recommender system based on the integration of subjective preferences and objective information. Appl Soft Comput 18:290–301

    Google Scholar 

  28. Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-nrecommendation tasks. In: Proceedings of the fourth ACM conference on Recommender systems. ACM, pp 39–46

  29. Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering, in proceedings of the 16th international conference on world wide web, WWW’07. Association for Computing Machinery, New York, pp 271–280

    Google Scholar 

  30. Dixon JK (1979) Pattern recognition with partly missing data. IEEE Trans Syst Man Cybern 9(10):617–621. https://doi.org/10.1109/TSMC.1979.4310090.

  31. Ebadi A, Krzyzak A (2016) A hybrid multi-criteria hotel recommender system using explicit and implicit feedbacks. World Acad Sci Eng Technol Int J Comput Electric Autom Control Inf Eng 10(8):1377–1385

    Google Scholar 

  32. Ekstrand MD, Harper FM, Willemsen MC, Konstan JA (2014) User perception of differences in recommender algorithms, in Eighth ACM conference on recommender systems, RecSys ‘14, Foster City, Silicon Valley, CA, 06–10 Oct 2014, pp. 161–168

  33. Feely C, Caulfield B, Lawlor A, Smyth B (2020) Using case-based reasoning to predict marathon performance and recommend tailored training plans, in Case-based reasoning Research and Development - 28th international conference, ICCBR 2020, Salamanca, Spain, June 8–12, 2020, ed. by I. Watson, R.O. Weber. Lecture Notes in Computer Science, vol. 12311 Springer, New York, pp. 67–81

  34. Fong Y-S, Pomalaza-Raez CA, Wang X-H (1989) Comparison study of nonlinear filters in image processing applications. Opt Eng 28(7):749. https://doi.org/10.1117/12.7977031

    Article  Google Scholar 

  35. Gallego A-J, Calvo-Zaragoza J, Rico-Juan JR (2020) Insights into efficient k-nearest neighbor classification with convolutional neural codes. IEEE Access 8:99312–99326. https://doi.org/10.1109/ACCESS.2020.2997387

    Article  Google Scholar 

  36. Garcia-Crespo A, Gomez-Berbis JM, Colomo-Palacios R, Garcia-Sanchez F (2009) Using support vector machines for feature-oriented profile-based recommendations. Int J Adv Intell Paradig 1(4):418–431. https://doi.org/10.1504/IJAIP.2009.026762

    Article  Google Scholar 

  37. Giaretta P (1995) Ontologies and knowledge bases towards a terminological clarification. Towards very large knowledge bases. Knowl Build Knowl Sharing 1995:25

    Google Scholar 

  38. Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70. https://doi.org/10.1145/138859.138867

    Article  Google Scholar 

  39. Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: a constant time collaborative filtering algorithm. Inf Retr 4(2):133–151

    Google Scholar 

  40. Gong S (2010) A collaborative filtering recommendation algorithm based on user clustering and item clustering. J Softw 5(7):745–752

    Google Scholar 

  41. Gunawardana A, Shani G (2009) A survey of accuracy evaluation metrics of recommendation tasks. J Mach Learn Res 10(Dec):2935–2962

    MathSciNet  Google Scholar 

  42. He Y, Yang S, Jiao C (2011) A hybrid collaborative filtering recommendation algorithm for solving the data sparsity. In: Computer science and society (ISCCS), 2011 international symposium, pp 118–121. https://doi.org/10.1109/ISCCS.2011.40

    Chapter  Google Scholar 

  43. Hearst MA (1992) Automatic acquisition of hyponyms from large text corpora. In: Paper presented at the proceedings of the 14th conference on computational linguistics-volume 2

    Google Scholar 

  44. Iaquinta L, De Gemmis M, Lops P, Semeraro G, Filannino M, Molino P (2008) Introducing serendipity in a content-based recommender system. In: Hybrid intelligent systems, 2008. HIS'08. Eighth international conference, pp 168–173. https://doi.org/10.1109/HIS.2008.25

  45. Jannach, D, Zanker, M, Felfernig, A, Friedrich, G (2010) Recommender systems: an introduction. Cambridge University Press

  46. Jawaheer, G, Szomszor, M, Kostkova, P (2010) Comparison of implicit and explicit feedback from an online music recommendation service. Paper presented at the proceedings of the 1st international workshop on information heterogeneity and fusion in recommender systems.

  47. Ji K, Sun R, Li X, Shu W (2016) Improving matrix approximation for recommendation via a clustering-based reconstructive method. Neurocomputing 173(Part 3):912–920

  48. Jiang, R (2013) From Ontology to Semantic Similarity: Calculation of Ontology-Based Semantic Similarity The Scientific World Journal, 2013

  49. Jorro-Aragoneses J, Caro-Martinez M, Recio-Garcia JA, Diaz-Agudo B, Jimenez-Diaz G (2019) Personalized case-based explanation of matrix factorization recommendations. In: Bach K, Marling C (eds) Case-based reasoning Research and Development. ICCBR 2019, Lecture notes in computer science, vol 11680. Springer, Cham. https://doi.org/10.1007/978-3-030-29249-2_10

    Chapter  Google Scholar 

  50. Jou JM, Chen P-Y, Sun J-M (1999) The gray prediction search algorithm for block motion estimation. IEEE Trans Circuits Syst Video Technol 9(6):843–848

    Google Scholar 

  51. Julong D (1989) Introduction to grey system theory. J Grey Syst 1:1–24

    MathSciNet  Google Scholar 

  52. Kilanioti I, Fernández-Montes A, Fernández-Cerero D, Karageorgos A (2019) Towards efficient and scalable data-intensive content delivery: state-of-the-art, issues and challenges. In: Kołodziej J, González-Vélez H (eds) High-performance modelling and simulation for big data applications, Lecture notes in computer science(), vol 11400. Springer, Cham. https://doi.org/10.1007/978-3-030-16272-6_4

    Chapter  Google Scholar 

  53. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model, in proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’08. Association for Computing Machinery, New York, pp 426–434

    Google Scholar 

  54. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. Int Comput IEEE 7(1):76–80

    Google Scholar 

  55. Liu L, Mehandjiev N, Xu DL (2011) Multi-criteria service recommendation based on user criteria preferences. In: RecSys '11: proceedings of the fifth ACM conference on recommender systems, pp 77–84. https://doi.org/10.1145/2043932.2043950

    Chapter  Google Scholar 

  56. Liu R, Chen M, Wang Q, Li X (2020) Robust rank constrained sparse learning: a graph-based method for clustering. ICASSP : 4217–4221

  57. Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137

    MathSciNet  Google Scholar 

  58. Lops, P, De Gemmis, M, Semeraro, G (2011a) Content-based recommender systems: state of the art and trends. In Recommender systems handbook (pp. 73–105). Springer US

  59. Lops P, de Gemmis M, Semeraro G (2011b) Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender Systems Handbook. Springer, New York, pp 73–105

    Google Scholar 

  60. MacQueen, J (1967) Some methods for classification and analysis of multivariate observations. In proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, no. 14, pp. 281-297)

  61. Maedche A, Staab S (2002) Measuring similarity between ontologies. In: Gómez-Pérez A, Benjamins VR (eds) Knowledge engineering and knowledge management: ontologies and the semantic web. EKAW 2002, Lecture notes in computer science(), vol 2473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45810-7_24

    Chapter  Google Scholar 

  62. Marlin BM, Zemel RS, Roweis S, Slaney M (2007) Collaborative filtering and the missing at random assumption, in proceedings of the twenty-third conference on uncertainty in artificial intelligence, UAI’07. AUAI Press, Arlington, pp 267–275

    Google Scholar 

  63. McSherry F, Mironov I (2009) Differentially private recommender systems: building privacy into the netflix prize contenders. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 627–636. https://doi.org/10.1145/1557019.1557090

  64. Meymandpour R, Davis J (2014) Lodify: a hybrid recommender system based on linked open data. In: Semantic web evaluation challenge. Communications in Computer and Information Science, p 475

    Google Scholar 

  65. Middleton SE, Shadbolt NR, De Roure DC (2004) Ontological user profiling in recommender systems. ACM Trans Inf Syst (TOIS) 22(1):54–88

    Google Scholar 

  66. Montaner M, López B, De La Rosa JL (2003) A taxonomy of recommender agents on the internet. Artif Intell Rev 19:285–330. https://doi.org/10.1023/A:1022850703159

    Article  Google Scholar 

  67. Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: DL '00: proceedings of the fifth ACM conference on digital libraries, pp 195–204. https://doi.org/10.1145/336597.336662

    Chapter  Google Scholar 

  68. Murthi B, Sarkar S (2003) The role of the management sciences in research on personalization. Manag Sci 49:1344–1362. https://doi.org/10.1287/mnsc.49.10.1344.17313

    Article  Google Scholar 

  69. Nilashi M, Bin Ibrahim O, Ithnin N (2014a) Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Syst Appl 41(8):3879–3900

    Google Scholar 

  70. Nilashi M, Bin Ibrahim O, Ithnin N (2014b) Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and neuro-fuzzy system. Knowl-Based Syst 60:82–101

    Google Scholar 

  71. Nilashi M, Bin Ibrahim O, Ithnin N, Sarmin NH (2015) A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS. Electron. Commer Res Appl 14(6):542–562

    Google Scholar 

  72. 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

    Google Scholar 

  73. Pan R, Yang T, Cao J et al (2015) Missing data imputation by K nearest neighbours based on grey relational structure and mutual information. Appl Intell 43:614–632. https://doi.org/10.1007/s10489-015-0666-x

    Article  Google Scholar 

  74. Paradarami TK, Bastian ND, Wightman JL (2017) A hybrid recommender system using artificial neural networks. Expert Syst Appl 83:300–313

    Google Scholar 

  75. Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072

    Google Scholar 

  76. Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites. Mach Learn 27(3):313–331

    Google Scholar 

  77. Pham MC, Cao Y, Klamma RM, Jarke A (2011) Clustering approach for collaborative filtering recommendation using social network analysis. J UCS 17:583–604

    Google Scholar 

  78. Pirasteh P, Hwang D, Jung JJ (2015) Exploiting matrix factorization to asymmetric user similarities in recommendation systems. Knowl-Based Syst 83:51–57. https://doi.org/10.1016/j.knosys.2015.03.006

    Article  Google Scholar 

  79. Powell, MJD (1981) Approximation theory and methods: Cambridge university press

  80. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: CSCW '94: Proceedings of the 1994 ACM conference on computer supported cooperative work, pp 175–186. https://doi.org/10.1145/192844.192905

    Chapter  Google Scholar 

  81. Ricci F (2018) Recommender systems: models and techniques. In: Alhajj R, Rokne JG (eds) Encyclopedia of social network analysis and mining, 2nd edn. Springer, New York

    Google Scholar 

  82. Rich E (1979) User modeling via stereotypes. Cogn Sci 3(4):329–354

    Google Scholar 

  83. Salton, G (1989) Automatic text processing: the transformation, analysis, and retrieval of: Addison-Wesley

  84. Sarwar, BM, Karypis, G, Konstan, J, Riedl, J (2002) Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. Paper presented at the Proceedings of the fifth international conference on computer and information technology

  85. Schafer, JB, Frankowski, D, Herlocker, J, Sen, S (2007) Collaborative filtering recommender systems the adaptive web (pp. 291–324): Springer

  86. Shapira B, Arazy O, Kumar N (2009) Improving social recommender systems. IT Professional 11(04):38–44. https://doi.org/10.1109/MITP.2009.76

    Article  Google Scholar 

  87. Shinde SK, Kulkarni U (2012) Hybrid personalized recommender system using centering-bunching based clustering algorithm. Expert Syst Appl 39(1):1381–1387

    Google Scholar 

  88. Solanki SK, Patel JT (2015) ‘A survey on association rule mining’, 2015 fifth international conference on Advanced Computing & Communication Technologies. Haryana 2015:212–216

    Google Scholar 

  89. Song Q, Shepperd M, Mair C (2005) Using grey relational analysis to predict software effort with small data sets. In: 11th IEEE international software METRICS symposium (METRICS'05), Como, p 10. https://doi.org/10.1109/METRICS.2005.51

  90. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:4

    Google Scholar 

  91. Su S-L, Su Y-C, Huang J-F (2000) Grey-based power control for ds-cdma cellular mobile systems. IEEE Trans Veh Technol 49:2081–2088

    Google Scholar 

  92. Tejeda-Lorente A, Porcel C, Bernabé-Moreno J, Herrera-Viedma E (2015) REFORE: a recommender system for researchers based on bibliometrics. Appl Soft Comput 30:778–791

    Google Scholar 

  93. Truong K, Ishikawa F, Honiden S (2007) Improving accuracy of recommender system by item clustering. IEICE Trans Inf Syst 90(9):1363–1373

    Google Scholar 

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

    Google Scholar 

  95. Van Meteren, R, Van Someren, M (2000) Using content-based filtering for recommendation. Paper presented at the proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop

  96. Wang P (2012) A personalized collaborative recommendation approach based on clustering of customers. Phys Procedia 24:812–816

    Google Scholar 

  97. Wang Q, He X, Jiang X, Li X (2022) Robust bi-stochastic graph regularized matrix factorization for data clustering. IEEE Trans Pattern Anal Mach Intell 44(1):390–403

    Google Scholar 

  98. Wei C-P, Yang C-S, Hsiao H-W (2008) A collaborative filtering-based approach to personalized document clustering. Decis Support Syst 45(3):413–428

    Google Scholar 

  99. Zhang S (2012) Nearest neighbor selection for iteratively knn imputation. J Syst Softw 85:2541–2552

    Google Scholar 

  100. Zhang C, Zhu X, Zhang J, Qin Y, Zhang S (2007) "Gbkii: An imputation method for missing values," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 1080–1087

  101. Zhang Z-K, Zhou T, Zhang Y-C (2011) Tag-aware recommender systems: a state-of-the-art survey. J Comput Sci Technol 26(5):767–777

    MathSciNet  Google Scholar 

  102. Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52(1):1–38

    Google Scholar 

  103. Ziakis C, Vlachopoulou M, Kyrkoudis T, Karagkiozidou M (2019) Important factors for improving Google search rank. Futur Int 11(2):32

    Google Scholar 

Download references

Acknowledgements

This work was supported by no grant.

Funding

The authors declare no competing financial interests.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Payam Bahrani & Mitra Mirzarezaee

  2. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Behrouz Minaei-Bidgoli

  3. Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Tehran, Iran

    Hamid Parvin

  4. Department of Electrical Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, 75169, Iran

    Ahmad Keshavarz

Authors
  1. Payam Bahrani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Behrouz Minaei-Bidgoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Parvin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mitra Mirzarezaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ahmad Keshavarz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PB designed the study; PB, BM, and HP wrote and edited the manuscript with help from MM, and AK. PB, BM, and HP carried out all the analyses, including the statistical analyses (with help from MM, and AK). PB generated all figures and tables. All authors have read and approved the final version of the paper.

Corresponding author

Correspondence to Hamid Parvin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bahrani, P., Minaei-Bidgoli, B., Parvin, H. et al. A hybrid semantic recommender system enriched with an imputation method. Multimed Tools Appl 83, 15985–16018 (2024). https://doi.org/10.1007/s11042-023-15258-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15258-4

Keywords

Search

Navigation