Skip to main content
SpringerLink
Log in

Use of Soft Computing Techniques for Recommender Systems: An Overview

  • Chapter
  • First Online:
Applications of Soft Computing for the Web

Abstract

Recommender systems (RSs) for the web are used to generate recommendations for a set of items that might be of interest to the user. RSs play an increasingly important role for a user in order to deal with information overload problem on the Web. Recent studies demonstrate that incorporation of soft computing techniques into traditional RSs can improve the accuracy of recommendations. This paper, therefore, presents a review of the field of recommendation systems that comprises soft computing approaches besides the typical user-item information used in most of the classical recommender systems. We also provide the classification for each technique, their ability to address the challenges, explain their framework, and discuss possible extensions to further improvement in the recommendation accuracy, which can be served as a roadmap for research in this area.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. Grosan C, Abraham A, Chis M (2006) Swarm intelligence in data mining. Springer, Berlin Heidelberg, In Swarm Intelligence in Data Mining, pp 1–20

    Book  Google Scholar 

  3. Bobadilla J, Ortega F, Hernando A, Alcalá J (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl Based Syst 24(8):1310–1316

    Article  Google Scholar 

  4. Tettamanzi A, Tomassini M (2013) Soft computing: integrating evolutionary, neural, and fuzzy systems. Springer Science & Business Media, Berlin

    Google Scholar 

  5. Abbas A, Zhang L, Khan SU (2015) A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97(7):667–690

    Google Scholar 

  6. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Google Scholar 

  7. Chang CC, Chen P-L, Chiu F-R, Chen Y-K (2009) Application of neural networks and Kano’s method to content recommendation in web personalization. Expert Systems with Appl 36(3):5310–5316

    Google Scholar 

  8. Horváth T, de Carvalho AC (2016) Evolutionary computing in recommender systems: a review of recent research. Nat Comput 1–22

    Google Scholar 

  9. Ar Y, Bostanci E (2016) A genetic algorithm solution to the collaborative filtering problem. Expert Syst Appl 61:122–128

    Google Scholar 

  10. Kennedy J (2011) Particle swarm optimization. Springer, US, In Encyclopedia of machine learning, pp 760–766

    Google Scholar 

  11. Wasid M, Kant V (2015) A particle swarm approach to collaborative filtering based recommender systems through fuzzy features. Procedia Comput Sci 54:440–448

    Article  Google Scholar 

  12. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Google Scholar 

  13. Lu J, Dianshuang W, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

    Article  Google Scholar 

  14. 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

    Google Scholar 

  15. Kant V, Bharadwaj KK (2013) A user-oriented content based recommender system based on reclusive methods and interactive genetic algorithm. In: Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012). Springer, India, p 543–554

    Google Scholar 

  16. De Campos LM, Fernández-Luna JM, Huete JF, Rueda-Morales MA (2010) Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. Int J Approximate Reasoning 51(7):785–799

    Google Scholar 

  17. Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: Proceedings of the fifth ACM conference on Digital libraries, ACM, p 195–204

    Google Scholar 

  18. Wasid M, Kant V, Ali R (2016) Frequency-based similarity measure for context-aware recommender systems. Communications and Informatics, IEEE, In International Conference on Advances in Computing, pp 627–632

    Google Scholar 

  19. Burke R (2007) Hybrid web recommender systems. Springer, Berlin Heidelberg, In The adaptive web, pp 377–408

    Google Scholar 

  20. Wang Z, Xue Y, Feng N, Wang Z (2014) An improved collaborative movie recommendation system using computational intelligence. J Vis Lang Comput 25(6):667–675

    Google Scholar 

  21. Cai Y, Leung H-F, Li Q, Min H, Tang J, Li J (2014) Typicality based collaborative filtering recommendation. IEEE Trans Knowl Data Eng 26(3):766–779

    Article  Google Scholar 

  22. Cao Y, Li Y (2007) An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst Appl 33(1):230–240

    Google Scholar 

  23. Celma O (2010) Music recommendation. Springer, Berlin Heidelberg, In Music Recommendation and Discovery, pp 43–85

    Book  Google Scholar 

  24. Zenebe A, Norcio AF (2009) Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst 160(1):76–94

    Google Scholar 

  25. Cornelis C, Lu J, Guo X, Zhang G (2007) One-and-only item recommendation with fuzzy logic techniques. Inf Sci 177(22):4906–4921

    Google Scholar 

  26. Morawski J, Stepan T, Dick S, Miller J (2017) A fuzzy recommender system for public library catalogs. Int J Intell Syst

    Google Scholar 

  27. Zhang Z, Lin H, Liu K, Dianshuang W, Zhang G, Jie L (2013) A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf Sci 235:117–129

    Article  Google Scholar 

  28. Castellano G, Fanelli AM, Plantamura P, Torsello MA (2008) A neuro-fuzzy strategy for web personalization. In AAAI, p 1784–1785

    Google Scholar 

  29. de Campos LM, Fernández-Luna JM, Huete JF (2008) A collaborative recommender system based on probabilistic inference from fuzzy observations. Fuzzy Sets Syst 159(12):1554–1576

    Google Scholar 

  30. 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

    Article  Google Scholar 

  31. Yager RR (2003) Fuzzy logic methods in recommender systems. Fuzzy Sets Syst 136(2):133–149

    Article  MathSciNet  MATH  Google Scholar 

  32. Kant V, Bharadwaj KK (2013) Fuzzy computational models of trust and distrust for enhanced recommendations. Int J Intell Syst 28(4):332–365

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. Porcel C, López-Herrera AG, Herrera-Viedma E (2009) A recommender system for research resources based on fuzzy linguistic modeling. Expert Syst Appl 36(3):5173–5183

    Google Scholar 

  35. Im KH, Park SC (2007) Case-based reasoning and neural network based expert system for personalization. Expert Syst Appl 32(1):77–85

    Article  Google Scholar 

  36. Christakou C, Vrettos S, Stafylopatis A (2007) A hybrid movie recommender system based on neural networks. Int J Artif Intell Tools 16(05):771–792

    Google Scholar 

  37. Deng Y, Wu Z, Tang C, Si H, Xiong H, Chen Z (2010) A hybrid movie recommender based on ontology and neural networks. In: Proceedings of the 2010 IEEE/ACM int’l conference on green computing and communications & int’l conference on cyber, physical and social computing, IEEE computer society, p 846–851

    Google Scholar 

  38. Strub F, Mary J, Gaudel R (2016) Hybrid Collaborative filtering with autoencoders

    Google Scholar 

  39. Wu C, Wang J, Liu J, Liu W (2016) Recurrent neural network based recommendation for time heterogeneous feedback. Knowl Based Syst 109:90–103

    Article  Google Scholar 

  40. Twardowski B (2016) Modelling contextual information in session-aware recommender systems with neural networks. In: Proceedings of the 10th ACM conference on recommender systems (ACM), p 273–276

    Google Scholar 

  41. da Silva EQ, Camilo-Junior CG, Pascoal LML, Rosa TC (2016) An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering. Expert Syst Appl 53:204–218

    Google Scholar 

  42. Kim K, Ahn H (2008) A recommender system using GA K-means clustering in an online shopping market. Expert Syst Appl 34(2):1200–1209

    Google Scholar 

  43. Anand D, Bharadwaj KK (2010) Adaptive user similarity measures for recommender systems: a genetic programming approach. In: 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 8. IEEE, p 121–125

    Google Scholar 

  44. Georgiou O, Tsapatsoulis N (2010) Improving the scalability of recommender systems by clustering using genetic algorithms. In: International conference on artificial neural networks. Springer, Berlin, Heidelberg, p 442–449

    Google Scholar 

  45. Marung U, Theera-Umpon N, Auephanwiriyakul S (2016) Top-N recommender systems using genetic algorithm-based visual-clustering methods. Symmetry 8(7):54

    Google Scholar 

  46. Fong, Simon, Yvonne Ho, and Yang Hang (2008) Using genetic algorithm for hybrid modes of collaborative filtering in online recommenders. In: Eighth international conference on hybrid intelligent systems, HIS’08, IEEE, p 174–179

    Google Scholar 

  47. Anand D, Bharadwaj KK (2010) Enhancing accuracy of recommender system through adaptive similarity measures based on hybrid features. In: Asian conference on intelligent information and database systems. Springer, Berlin, Heidelberg, p 1–10

    Google Scholar 

  48. Ujjin S, Bentley PJ (2002) Learning user preferences using evolution. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, Singapore

    Google Scholar 

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

    Google Scholar 

  50. Cui L, Ou P, Fu X, Wen Z, Lu N (2016) A novel multi-objective evolutionary algorithm for recommendation systems. J Parallel Distrib Comput

    Google Scholar 

  51. Anand D, Bharadwaj KK (2011) Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Syst Appl 38(5):5101–5109

    Article  Google Scholar 

  52. Alhijawi B, Yousef K (2016) Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems”, In: IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), IEEE, p 1–6

    Google Scholar 

  53. Anand D (2012) Feature extraction for collaborative filtering: a genetic programming approach. Int J Comput Sci Issues 9(1)

    Google Scholar 

  54. Chong CS, Zhang T, Lee KK, Hung GG, Lee B-S (2013) Collaborative analytics with genetic programming for workflow recommendation. In: IEEE International conference on systems, man, and cybernetics (SMC), IEEE, p 657–662

    Google Scholar 

  55. Michalski RS (2000) Learnable evolution model: Evolutionary processes guided by machine learning. Mach Learn 38(1–2):9–40

    Google Scholar 

  56. Ujjin S, Bentley PJ (2003) Particle swarm optimization recommender system. In: Proceedings of the 2003 Swarm Intelligence Symposium (SIS’03, IEEE), p 124–131

    Google Scholar 

  57. Bakshi S, Jagadev AK, Dehuri S, Wang GN (2014) Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization. Appl Soft Comput 15:21–29

    Google Scholar 

  58. Alam S, Dobbie G, Koh YS, Riddle P (2014) Web usage mining based recommender systems using implicit heterogeneous data. Web Intell Agent Syst Int J 12(4):389–409

    Google Scholar 

  59. Alam, S, Dobbie G, Riddle P (2011) Towards recommender system using particle swarm optimization based web usage clustering. In: Pacific-Asia conference on knowledge discovery and data mining, Springer, Berlin, Heidelberg, p 316–326

    Google Scholar 

  60. Katarya R, Verma OP (2016) A collaborative recommender system enhanced with particle swarm optimization technique. Multimedia Tools Appl 75(15):9225–9239

    Article  Google Scholar 

  61. Tyagi S, Bharadwaj KK (2014) A particle swarm optimization approach to fuzzy case-based reasoning in the framework of collaborative filter-ing. Int J Rough Sets Data Anal (IJRSDA) 1(1):48–64

    Article  Google Scholar 

  62. Tyagi S, Bharadwaj KK (2013) Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining. Swarm Evol Comput 13:1–12

    Article  Google Scholar 

  63. Bedi P, Sharma R (2012) Trust based recommender system using ant colony for trust computation. Expert Syst Appl 39(1):1183–1190

    Article  Google Scholar 

  64. Gohari FS, Haghighi H, Aliee FS (2016) A semantic-enhanced trust based recommender system using ant colony optimization. Appl Intell 1–37

    Google Scholar 

  65. Tengkiattrakul P, Maneeroj S, Takasu A (2016) Applying ant-colony concepts to trust-based recommender systems. In: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services (ACM), p 34–41

    Google Scholar 

  66. Chen Z, Shao Z, Xie Z, Huang X (2010) An attribute-based scheme for service recommendation using association rules and ant colony algorithm. In: Wireless Telecommunications Symposium (WTS) IEEE, p 1–6

    Google Scholar 

  67. Zhao Q, Zhang Y, Chen J (2016) An improved ant colony optimization algorithm for recommendation of micro-learning path. In: IEEE international conference on computer and information technology (CIT) IEEE, p 190–196

    Google Scholar 

  68. Sobecki J, Tomczak JM (2010) Student courses recommendation using ant colony optimization. In: Asian conference on intelligent information and database systems. Springer, Berlin, Heidelberg, p 124–133

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Aligarh Muslim University, Aligarh, 202002, Uttar Pradesh, India

    Mohammed Wasid & Rashid Ali

Authors
  1. Mohammed Wasid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rashid Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed Wasid .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    Rashid Ali

  2. Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    MM Sufyan Beg

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wasid, M., Ali, R. (2017). Use of Soft Computing Techniques for Recommender Systems: An Overview. In: Ali, R., Beg, M. (eds) Applications of Soft Computing for the Web. Springer, Singapore. https://doi.org/10.1007/978-981-10-7098-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7098-3_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7097-6

  • Online ISBN: 978-981-10-7098-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation