Skip to main content
SpringerLink
Log in

Effective methods for increasing aggregate diversity in recommender systems

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

Abstract

In order to make a recommendation, a recommender system typically first predicts a user’s ratings for items and then recommends a list of items to the user which have high predicted ratings. Quality of predictions is measured by accuracy, that is, how close the predicted ratings are to actual ratings. On the other hand, quality of recommendation lists is evaluated from more than one perspective. Since accuracy of predicted ratings is not enough for customer satisfaction, metrics such as novelty, serendipity, and diversity are also used to measure the quality of the recommendation lists. Aggregate diversity is one of these metrics which measures the diversity of items across the recommendation lists of all users. Increasing aggregate diversity is important because it leads a more even distribution of items in the recommendation lists which prevents the long-tail problem. In this study, we propose two novel methods to increase aggregate diversity of a recommender system. The first method is a reranking approach which takes a ranked list of recommendations of a user and reranks it to increase aggregate diversity. While the reranking approach is applied after model generation as a wrapper the second method is applied in model generation phase which has the advantage of being more efficient in the generation of recommendation lists. We compare our methods with the well-known methods in the field and show the superiority of our methods using real-world datasets.

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

Similar content being viewed by others

Notes

  1. Source code will be available at https://github.com/mkarakaya/graphaggregatediversityafterpublication.

  2. http://grouplens.org/datasets/movielens/1m/.

  3. http://grouplens.org/datasets/book-crossing/.

  4. http://webscope.sandbox.yahoo.com/catalog.php?datatype=r.

References

  1. Adomavicius G, Kwon Y (2011) Maximizing aggregate recommendation diversity: a graph-theoretic approach. In: Proceedings of workshop on novelty and diversity in recommender systems, Chicago, pp 3–10

  2. Adomavicius G, Kwon Y (2012) Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans Knowl Data Eng 24(5):896–911

    Article  Google Scholar 

  3. Adomavicius G, Kwon Y (2014) Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS J Comput 26(2):351–369. https://doi.org/10.1287/ijoc.2013.0570

    Article  MathSciNet  MATH  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. Adomavicius G, Zhang J (2015) Improving stability of recommender systems: a meta-algorithmic approach. IEEE Trans Knowl Data Eng 276:1573–1587

    Article  Google Scholar 

  6. Anderson C (2006) The long tail. Wired (October 2004)

  7. Aytekin T, Karakaya MÖ (2014) Clustering-based diversity improvement in top-n recommendation. J Intell Inf Syst 42(1):118. https://doi.org/10.1007/s10844-013-0252-9

    Article  Google Scholar 

  8. Bokde DK, Girase S, Mukhopadhyay D (2015) An approach to a university recommendation by multi-criteria collaborative filtering and dimensionality reduction techniques. In: 2015 IEEE international symposium on nanoelectronic and information systems. IEEE

  9. Bradley K, Smyth B (2001) Improving recommendation diversity. In: Proceedings of the 12th Irish conference on artificial intelligence and cognitive science

  10. Brynjolfsson E, Hu Y, Smith MD (2010) Research commentary-long tails vs. superstars: the effect of information technology on product variety and sales concentration patterns. Inf Syst Res 21(4):736–747

    Article  Google Scholar 

  11. Celma Ò (2010) Music recommendation and discovery–the long tail, long fail, and long play in the digital music space. Springer, Berlin. https://doi.org/10.1007/978-3-642-13287-2

    Google Scholar 

  12. Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, Berlin, pp 107–144. http://www.springerlink.com/content/978-0-387-85819-7

  13. Fleder D, Hosanagar K (2009) Blockbuster cultures next rise or fall: the impact of recommender systems on sales diversity. Manag Sci 55(5):697–712

    Article  Google Scholar 

  14. Fouss F (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng 19(3):355–369

    Article  Google Scholar 

  15. Haifeng L et al (2015) Trust-aware recommendation for improving aggregate diversity. New Rev Hypermedia Multimed 21(3–4):242–258

    Google Scholar 

  16. Hurley N, Zhang M (2011) Novelty and diversity in top-n recommendation—analysis and evaluation. ACM Trans Internet Technol 10(4):14

    Article  Google Scholar 

  17. Hwang S, Wei C, Liao Y (2010) Coauthorship networks and academic literature recommendation. Electron Commer Res Appl 9(4):323–334. https://doi.org/10.1016/j.elerap.2010.01.001

    Article  Google Scholar 

  18. Javari A, Jalili M (2015) A probabilistic model to resolve diversityaccuracy challenge of recommendation systems. Knowl Inf Syst 443:609–627

    Article  Google Scholar 

  19. Kim HK, Oh HY, Gu J, Kim JK (2011) Commenders: a recommendation procedure for online book communities. Electron Commer Res Appl 10(5):501–509. https://doi.org/10.1016/j.elerap.2011.03.002

    Article  Google Scholar 

  20. Koren Y (2010) Collaborative filtering with temporal dynamics. Commun ACM 53(4):89–97

    Article  Google Scholar 

  21. Koren Y, Bell RM (2011) Advances in collaborative filtering. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, Berlin, pp 145–186. http://www.springerlink.com/content/978-0-387-85819-7

  22. Koren Y, Bell RM, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Computer 42(8):30–37. https://doi.org/10.1109/MC.2009.263

    Article  Google Scholar 

  23. Kwon K, Kim C (2012) How to design personalization in a context of customer retention: who personalizes what and to what extent? Electron Commer Res Appl 11(2):101–116. https://doi.org/10.1016/j.elerap.2011.05.002

    Article  Google Scholar 

  24. Lekakos G, Caravelas P (2008) A hybrid approach for movie recommendation. Multimed Tools Appl 36(1–2):55–70. https://doi.org/10.1007/s11042-006-0082-7

    Article  Google Scholar 

  25. Linden G, Smith B, York J (2003) Amazon. com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):680

    Article  Google Scholar 

  26. Liu J, Dolan P, Pedersen ER (2010) Personalized news recommendation based on click behavior. In: Rich C, Yang Q, Cavazza M, Zhou MX (eds) Proceedings of the 2010 international conference on intelligent user interfaces. ACM, Hong Kong, pp 31–40. https://doi.org/10.1145/1719970.1719976

    Google Scholar 

  27. Majid A, Chen L, Chen G, Mirza HT, Hussain I, Woodward J (2013) A context-aware personalized travel recommendation system based on geotagged social media data mining. Int J Geogr Inf Sci 27(4):662–684. https://doi.org/10.1080/13658816.2012.696649

    Article  Google Scholar 

  28. McNee JR Sean M, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI06 extended abstracts on Human factors in computing systems. ACM

  29. Mirza BJK, Batul J, Ramakrishnan N (2003) Studying recommendation algorithms by graph analysis. J Intell Inf Syst 20(2):131–160

    Article  Google Scholar 

  30. Niemann K, Wolpers M (2013) A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In: ACM SIGKDD international conference on knowledge discovery and data mining, ACM, Chicago, pp 955–963. https://doi.org/10.1145/2487575.2487656

  31. Park YJ, Tuzhilin A (2008) The long tail of recommender systems and how to leverage it. In: Proceedings of the 2008 ACM conference on Recommender systems. ACM

  32. Patil CB, Wagh RB (2014) A multi-attributed hybrid re-ranking technique for diversified recommendations. In: 2014 IEEE international conference electronics computing and communication technologies (IEEE CONECCT)

  33. Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2011) Recommender systems handbook. Springer, Berlin http://www.springerlink.com/content/978-0-387-85819-7

  34. Sarwar B, Karypis G, Konstan J, Riedl J (2002) Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Fifth international conference on computer and information science. Citeseer, p 2728

  35. Schwartz MF, Wood D (1993) Discovering shared interests using graph analysis. Commun ACM 36(8):78–89

    Article  Google Scholar 

  36. Smyth B, McClave P (2001) Similarity vs. diversity. In: Aha DW, Watson I (eds) Proceedings of the 4th international conference on case-based reasoning. Lecture notes in computer science, vol 2080. Springer, Vancouver, Canada, pp 347–361

  37. Ziegler CN, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on world wide web, Chiba, pp 22–32

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Bahçeşehir University, Çırağan Caddesi, 34353, Beşiktaş, İstanbul, Turkey

    Mahmut Özge Karakaya & Tevfik Aytekin

Authors
  1. Mahmut Özge Karakaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tevfik Aytekin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmut Özge Karakaya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karakaya, M.Ö., Aytekin, T. Effective methods for increasing aggregate diversity in recommender systems. Knowl Inf Syst 56, 355–372 (2018). https://doi.org/10.1007/s10115-017-1135-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-017-1135-0

Keywords

Search

Navigation