Skip to main content
SpringerLink
Log in

Comparison of the Non-personalized Active Learning Strategies Used in Recommender Systems

  • Conference paper
  • First Online:
Information Systems (EMCIS 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 341))

  • 1261 Accesses

Abstract

The study of recommender systems is essential nowadays due to its great effect on businesses and customer satisfaction. Different active learning strategies were previously developed to gain ratings from the users on specific items, and this enables the system to have more information and consequently make more accurate recommendations. In previous studies, these strategies were evaluated using a different selection of metrics in each work, and the experimentations were done on different datasets. In this paper, we solve these weaknesses by comparing the main ten non-personalized strategies on a fair ground, by simulating them against two datasets and using seven of the mostly agreed upon metrics. This gives more trust and less biased results when comparing their performances. Also, the analysis of the computation time and the elicitation efficiency is added.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_3

    Chapter  Google Scholar 

  2. de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-aware content-based recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 119–159. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_4

    Chapter  Google Scholar 

  3. Wang, Y., Chan, S.C.-F., Ngai, G.: Applicability of demographic recommender system to tourist attractions: a case study on trip advisor. In: Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, WI-IAT 2012, vol. 03, pp. 97–101. IEEE Computer Society, Washington, DC (2012)

    Google Scholar 

  4. Beliakov, G., Calvo, T., James, S.: Aggregation functions for recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 777–808. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_23

    Chapter  Google Scholar 

  5. Burke, R.: Knowledge-based recommender systems (2000)

    Google Scholar 

  6. Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: Constraint-based recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 161–190. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_5

    Chapter  Google Scholar 

  7. Adomavicius, G., Tuzhilin, A.: 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 (2005)

    Article  Google Scholar 

  8. Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 77–118. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_3

    Chapter  Google Scholar 

  9. Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_4

    Chapter  Google Scholar 

  10. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)

    Article  Google Scholar 

  11. Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits wordnet-based user profiles for neighborhood formation. User Model. User-Adap. Inter. 17(3), 217–255 (2007)

    Article  Google Scholar 

  12. Adomavicius, G., Kwon, Y.: Multi-criteria recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 847–880. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_25

    Chapter  Google Scholar 

  13. Elahi, M., Ricci, F., Repsys, V.: System-wide effectiveness of active learning in collaborative filtering. In: Bonchi, F., Buntine, W., Gavald, R., Gu, S. (eds.) International Workshop on Social Web Mining, Co-located with IJCAI, Universitat de Barcelona, Spain, p. 1 (2011)

    Google Scholar 

  14. Kutty, S., Yu, F.: Recommendations and predictions with ET greedy active learning. Technical report, Electrical Engineering and Computer Science Department of University of Michigan (2009)

    Google Scholar 

  15. Elahi, M., Ricci, F., Rubens, N.: A survey of active learning in collaborative filtering recommender systems. Comput. Sci. Rev. 20, 29–50 (2016)

    Article  MathSciNet  Google Scholar 

  16. Kohrs, A., Merialdo, B.: Improving collaborative filtering for new users by smart object selection. In: Proceedings of International Conference on Media Features, ICMF (2001)

    Google Scholar 

  17. Rubens, N., Sugiyama, M.: Influence-based collaborative active learning. In: Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys 2007, pp. 145–148. ACM, New York (2007)

    Google Scholar 

  18. Rashid, A.M., et al.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, IUI 2002, pp. 127–134. ACM, New York (2002)

    Google Scholar 

  19. Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explor. Newsl. 10, 90–100 (2008)

    Article  Google Scholar 

  20. Golbandi, N., Koren, Y., Lempel, R.: On bootstrapping recommender systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1805–1808. ACM, New York (2010)

    Google Scholar 

  21. Liu, N.N., Meng, X., Liu, C., Yang, Q.: Wisdom of the better few: cold start recommendation via representative based rating elicitation. In: Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, pp. 37–44 (2011)

    Google Scholar 

  22. Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19 (2015)

    Article  Google Scholar 

  23. Elahi, M., Repsys, V., Ricci, F.: Rating elicitation strategies for collaborative filtering. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 160–171. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23014-1_14

    Chapter  Google Scholar 

  24. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pp. 175–186. ACM, New York (1994)

    Google Scholar 

  25. Massa, P., Avesani, P.: Trust metrics in recommender systems. In: Golbeck, J. (ed.) Computing with Social Trust, pp. 259–285. Springer, London (2009). https://doi.org/10.1007/978-1-84800-356-9_10

    Chapter  Google Scholar 

  26. Schröder, G., Thiele, M., Lehner, W.: Setting goals and choosing metrics for recommender system evaluations. In: UCERSTI2 Workshop at the 5th ACM Conference on Recommender Systems, Chicago, USA (2011)

    Google Scholar 

  27. del Olmo, F.H., Gaudioso, E.: Evaluation of recommender systems: a new approach. Expert Syst. Appl. 35(3), 790–804 (2008)

    Article  Google Scholar 

  28. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)

    Article  Google Scholar 

  29. Powers, D.M.: Evaluation: from precision, recall and f-factor to roc, informedness, markedness and correlation. Technical report, School of Informatics and Engineering, Flinders University Adelaide, South Australia (2007)

    Google Scholar 

  30. Hossein, M., Shahraki, N., Bahadorpour, M.: Cold-start problem in collaborative recommender systems: efficient methods based on ask-to-rate technique. J. Comput. Inf. Technol. - CIT 22(2), 105–113 (2014)

    Article  Google Scholar 

  31. Chaaya, G., Metais, E., Bou Abdo, J., Chiky, R., Demerjian, J., Barbar, K.: Evaluating non-personalized single-heuristic active learning strategies for collaborative filtering recommender systems. In: 16th IEEE International Conference on Machine Learning and Applications, Cancun, Mexico (2017)

    Google Scholar 

  32. Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, Paul B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_8

    Chapter  Google Scholar 

  33. Chaaya, G., Bou Abdo, J., Demerjian, J., Chiky, R., Metais, E., Barbar, K.: An improved non-personalized combined-heuristic strategy for collaborative filtering recommender systems. In: IEEE Middle East & North Africa COMMunications Conference, MENACOMM (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CEDRIC Laboratory, CNAM, Paris, France

    Georges Chaaya & Elisabeth Métais

  2. Faculty of Natural and Applied Sciences, Notre Dame University, Deir El Kamar, Lebanon

    Jacques Bou Abdo

  3. LISITE Laboratory, ISEP, Paris, France

    Raja Chiky

  4. LARIFA-EDST Laboratory, Faculty of Sciences, Lebanese University, Fanar, Lebanon

    Jacques Demerjian & Kablan Barbar

Authors
  1. Georges Chaaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacques Bou Abdo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elisabeth Métais
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raja Chiky
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jacques Demerjian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kablan Barbar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Georges Chaaya .

Editor information

Editors and Affiliations

  1. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. University of Coimbra, Coimbra, Portugal

    Paulo Rupino da Cunha

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chaaya, G., Abdo, J.B., Métais, E., Chiky, R., Demerjian, J., Barbar, K. (2019). Comparison of the Non-personalized Active Learning Strategies Used in Recommender Systems. In: Themistocleous, M., Rupino da Cunha, P. (eds) Information Systems. EMCIS 2018. Lecture Notes in Business Information Processing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-11395-7_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-11395-7_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11394-0

  • Online ISBN: 978-3-030-11395-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation