Skip to main content

Book Recommender System Using Fuzzy Linguistic Quantifiers

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

Abstract

The recommender systems are used to facilitate the users with appropriate choices according to their preferences for various online services. Due to the increasing need, various recommendation systems have been developed including recommendation for music, book, movie, etc. The book recommendation technique usually explores the rating of the users for the particular product to recommend it to other users. Instead of utilizing users’ reviews, we have proposed an authorities recommendation approach which exploits ranking of the books by different top-ranked universities. These rankings are aggregated using OWA. Ordered Weighted Aggregation (OWA), a well-known fuzzy averaging operator, is used to aggregate different rankings of the books given by respective universities. The rank of the books is converted into scores using Positional Aggregation based Scoring (PAS) technique. The linguistic quantifiers are applied over these scores and the value of three linguistic quantifiers, ‘at least half’, ‘most’ and ‘as many as possible’, are compared with amazon ranking, evaluated on the basis of ranks explicitly taken from experts. P@10, FPR@10 and Mean Average Precision (MAP) are evaluated. It is evident from the results that quantifier ‘at least half’ outperformed others in the aforementioned performance metric. It is envisaged that the proposed approach will help the research community in designing the recommender systems to explore the relevant books and meet the expectation of the users in a better way.

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

Access this chapter

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, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Article  Google Scholar 

  2. 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

    Google Scholar 

  3. Burke R (2007) Hybrid web recommender systems. Springer, Berlin, pp 377–408

    Google Scholar 

  4. Burke R, Felfernig A, Göker MH (1997) Recommender systems: an overview, AI magazines, 32(3). pp. 13–18

    Google Scholar 

  5. Ali R (2013) Pro-Mining: Product recommendation using web based opinion mining. Int J Comput Eng Tech 4(6):299–313

    Google Scholar 

  6. Sohail SS, Siddiqui J, Ali R (2014) User feedback scoring and evaluation of a product recommendation system. In: In contemporary computing (IC3), 2014 seventh international conference on IEEE, pp 525–530

    Google Scholar 

  7. Sohail SS, Siddiqui J, Ali R (2015) UMW: a model for enhancement in wearable technology based on opinion mining technique. In: 12th international conference on learning and technology, Jeddah, KSA, © IEEE, April 2015, pp 9–13

    Google Scholar 

  8. Sohail SS, Siddiqui J, Ali R (2015) User Feedback Based Evaluation of a Product Recommendation System Using Rank Aggregation Method. Springer International Publishing, In Advances in Intelligent Informatics, pp 349–358

    Google Scholar 

  9. Liu DR, Shih YY (2005) Integrating AHP and data mining for product recommendation based on customer lifetime value. Inf Manag 42:387–400

    Article  Google Scholar 

  10. Weng SS, Liu MJ (2004) Feature-based recommendation for one-to-one marketing. Expert Syst Appl 26(4):493–508

    Article  Google Scholar 

  11. Cheung KW, Kwok JT, Law MH, Tsui KC (2003) Mining customer product ratings for personalized marketing. Decis Support Syst 35(2):231–243

    Article  Google Scholar 

  12. Goldberg D, Nichols D, Oki BM, Terry D (1992) Using Collaborative Filtering to Weave an Information TAPESTRY. Commun ACM 35(12):61–70

    Article  Google Scholar 

  13. Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: Proceedings fifth ACM conference on digital libraries, San Antonio, USA, 2–7 June 2000, pp 195–204

    Google Scholar 

  14. Jomsri P (2014) Book recommendation system for digital library based on user profiles by using association rule. In: Proceedings 2014 fourth international conference on innovative computing technology (INTECH), 13–15 August 2014, pp 130–134

    Google Scholar 

  15. Tewari AS, Priyanka K (2014) Book recommendation system based on collaborative filtering and association rule mining for college students. In: Proceedings 2014 international conference on contemporary computing and informatics (IC3I), 27–29 Nov 2014, pp 135–138

    Google Scholar 

  16. Mikawa M, Izumi S, Tanaka K (2011) Book recommendation signage system using silhouette-based gait classification. In: Proceedings 10th international conference on machine learning and applications, pp 416–419

    Google Scholar 

  17. Sohail SS, Siddiqui J, Ali R (2014) Ordered ranked weighted aggregation based book recommendation technique : a link mining approach. In: 14th international conference on hybrid intelligent systems (HIS), IEEE, pp 309–314

    Google Scholar 

  18. Sohail SS, Siddiqui J, Ali R (2016) Book recommender system using fuzzy linguistic quantifier and opinion mining. In: The international symposium on intelligent systems technologies and applications, Springer International Publishing, pp 573–583

    Google Scholar 

  19. Sohail SS, Siddiqui J, Ali R (2013) Book recommendation system using opinion mining technique. In: Advances in Computing, communications and informatics (ICACCI), 2013 international conference on IEEE, 2013, pp 1609–1614

    Google Scholar 

  20. Sohail SS, Siddiqui J, Ali R (2015) OWA based Book Recommendation Technique. Procedia Comput Sci 62:126–133

    Article  Google Scholar 

  21. Kim JK, Kim HK, Oh HY, Ryu YU (2010) A group recommendation system for online communities. Int J Inf Manag 30:212–219

    Article  Google Scholar 

  22. Borda JC (1781) Memoire sur les election au scrutiny. Histoire de l’Academie Royale des Sciences

    Google Scholar 

  23. Dwork C, Kumar R, Naor M, Sivakumar D Rank aggregation methods for the web. In Proceedings of the tenth international conference on world wide web, Hong Kong, 1–5 May, pp 613–622

    Google Scholar 

  24. Beg MMS, Ahmad N (2003) Soft computing techniques for rank aggregation on the World Wide Web. World Wide Web Int J 6(1):5–22

    Article  Google Scholar 

  25. Yager R (1988) On ordered weighted averaging aggregation operators in multicriteria decision Making. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  MATH  Google Scholar 

  26. Beliakov G, Pradera A, Calvo T (2007) Aggregation functions: a guide for practitioners. Springer, Heidelberg

    MATH  Google Scholar 

  27. Beg MMS (2005) User feedback based enhancement in web search quality. Inf Sci 170(2–4):153–172

    MathSciNet  Google Scholar 

  28. Malczewski J (2006) Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. Int J Appl Earth Obs Geoinf 8(4):270–277

    Article  Google Scholar 

  29. Makropoulos CK, Butler D (2006) Spatial ordered weighted averaging: incorporating spatially variable attitude towards risk in spatial multi-criteria decision-making. Environ Model Softw 21(1):69–84

    Article  Google Scholar 

  30. Ahamad G, Naqvi SK, Beg MM (2015) An OWA‐based model for talent enhancement in cricket. Int J Intell Syst 31(8):763–785

    Google Scholar 

  31. Sohail SS, Siddiqui J, Ali R (2017) A novel approach for book recommendation using fuzzy based aggregation. Indian J Sci Tech 8:1–30

    Article  Google Scholar 

  32. http://www.topuniversities.com/university-rankings/university-subject-rankings/2015/computer-science-information-systems#sorting=rank+region=+country=96+faculty=+stars=false+search=

  33. Sohail SS, Siddiqui J, Ali R An OWA based ranking approach for university books recommendation. Int J Intel Syst https://doi.org/10.1002/int.21937 (in press)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shahab Saquib Sohail .

Editor information

Editors and Affiliations

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

Sohail, S.S., Siddiqui, J., Ali, R. (2017). Book Recommender System Using Fuzzy Linguistic Quantifiers. 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_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7098-3_4

  • 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