Skip to main content
SpringerLink
Log in

Building Information Systems Using Collaborative-Filtering Recommendation Techniques

  • Conference paper
  • First Online:
Advanced Information Systems Engineering Workshops (CAiSE 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 349))

Included in the following conference series:

Abstract

IoT-technologies allow for the connection of miscellaneous devices, thereby creating a platform that sustains rich data sources. Given the circumstances, it is essential to have decent machinery in order to exploit the existing infrastructure and provide users with personalized services. Among others, recommender systems have been widely used to suggest users additional items that best match their needs and expectation. The use of recommender systems has gained considerable momentum in recent years. Nevertheless, the selection of a proper recommendation technique depends much on the input data as well as the domain of applications. In this work, we present an evaluation of two well-known collaborative-filtering (CF) techniques to build an information system for managing and recommending books in the IoT context. To validate the performance, we conduct a series of experiments on two considerably large datasets. The experimental results lead us to some interesting conclusions. In contrast to many existing studies which state that the item-based CF technique outperforms the user-based CF technique, we found out that there is no distinct winner between them. Furthermore, we confirm that the performance of a CF recommender system may be good with regards to some quality metrics, but not to some others.

The research described in this paper has been carried out as part of the CROSSMINER Project, EU Horizon 2020 Research and Innovation Programme, grant agreement No. 732223.

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

Notes

  1. 1.

    John Naisbitt, researcher of future studies.

  2. 2.

    https://github.com/BookRec/BookRec/.

  3. 3.

    http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

  4. 4.

    https://github.com/zygmuntz/goodbooks-10k.

References

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

    Article  Google Scholar 

  2. Aggarwal, C.C.: Neighborhood-based collaborative filtering. Recommender Systems, pp. 29–70. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3_2

    Chapter  Google Scholar 

  3. Anderson, C.: The Long Tail: Why the Future of Business is Selling Less of More. Hyperion (2006)

    Google Scholar 

  4. Bellogín, A., Cantador, I., Castells, P.: A comparative study of heterogeneous item recommendations in social systems. Inf. Sci. 221, 142–169 (2013)

    Article  MathSciNet  Google Scholar 

  5. Cacheda, F., Carneiro, V., Fernández, D., Formoso, V.: Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web 5(1), 2:1–2:33 (2011)

    Article  Google Scholar 

  6. Davidson, J., et al.: The youtube video recommendation system. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 293–296. ACM, New York (2010)

    Google Scholar 

  7. Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 233–240. ACM (2006)

    Google Scholar 

  8. Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)

    Article  Google Scholar 

  9. Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems, I-SEMANTICS 2012, pp. 1–8. ACM (2012)

    Google Scholar 

  10. Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6(4), 13:1–13:19 (2015)

    Article  Google Scholar 

  11. Isinkaye, F., Folajimi, Y., Ojokoh, B.: Recommendation systems: Principles, methods and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015)

    Article  Google Scholar 

  12. Jalili, M., Ahmadian, S., Izadi, M., Moradi, P., Salehi, M.: Evaluating collaborative filtering recommender algorithms: a survey. IEEE Access 6, 74003–74024 (2018)

    Article  Google Scholar 

  13. Karypis, G.: Evaluation of item-based top-N recommendation algorithms. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, CIKM 2001, pp. 247–254. ACM, New York (2001)

    Google Scholar 

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

    Article  Google Scholar 

  15. Nanopoulos, A., Rafailidis, D., Symeonidis, P., Manolopoulos, Y.: MusicBox: personalized music recommendation based on cubic analysis of social tags. IEEE Trans. Audio Speech Lang. Process. 18(2), 407–412 (2010)

    Article  Google Scholar 

  16. Nguyen, P.T., Tomeo, P., Di Noia, T., Di Sciascio, E.: An evaluation of SimRank and personalized PageRank to build a recommender system for the web of data. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 1477–1482. ACM (2015)

    Google Scholar 

  17. Nguyen, P.T., Tomeo, P., Di Noia, T., Di Sciascio, E.: Content-based recommendations via DBpedia and freebase: a case study in the music domain. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 605–621. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_35

    Chapter  Google Scholar 

  18. Niemann, K., Wolpers, M.: A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 955–963. ACM (2013)

    Google Scholar 

  19. Di Noia, T., Ostuni, V.C.: Recommender systems and linked open data. In: Faber, W., Paschke, A. (eds.) Reasoning Web 2015. LNCS, vol. 9203, pp. 88–113. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21768-0_4

    Chapter  Google Scholar 

  20. Noia, T.D., Rosati, J., Tomeo, P., Sciascio, E.D.: Adaptive multi-attribute diversity for recommender systems. Inf. Sci. 382–383, 234–253 (2017)

    Article  Google Scholar 

  21. Núñez-Valdéz, E.R., Lovelle, J.M.C., Martínez, O.S., García-Díaz, V., de Pablos, P.O., Marín, C.E.M.: Implicit feedback techniques on recommender systems applied to electronic books. Comput. Hum. Behav. 28(4), 1186–1193 (2012)

    Article  Google Scholar 

  22. Papagelis, M., Plexousakis, D.: Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS (LNAI), vol. 3191, pp. 152–166. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30104-2_12

    Chapter  Google Scholar 

  23. Saracevic, T.: Evaluation of evaluation in information retrieval. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1995, pp. 138–146. ACM (1995)

    Google Scholar 

  24. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 285–295. ACM, New York (2001)

    Google Scholar 

  25. Fortino, G., Rovella, A., Russo, W., Savaglio, C.: Towards cyberphysical digital libraries: integrating IoT smart objects into digital libraries. In: Guerrieri, A., Loscri, V., Rovella, A., Fortino, G. (eds.) Management of Cyber Physical Objects in the Future Internet of Things. IT, pp. 135–156. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26869-9_7

    Chapter  Google Scholar 

  26. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9

    Chapter  Google Scholar 

  27. Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 109–116. ACM (2011)

    Google Scholar 

  28. Vargas, S., Castells, P.: Improving sales diversity by recommending users to items. In: Eighth ACM Conference on Recommender Systems, RecSys 2014, Foster City, Silicon Valley, CA, USA, 06 October 2014, pp. 145–152 (2014)

    Google Scholar 

  29. Zajac, Z.: Goodbooks-10k: a new dataset for book recommendations. FastML (2017)

    Google Scholar 

  30. Zhao, Z.-D., Shang, M.-S.: User-based collaborative-filtering recommendation algorithms on hadoop. In: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining, WKDD 2010, pp. 478–481. IEEE Computer Society, Washington, DC (2010)

    Google Scholar 

  31. Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, WWW 2005, pp. 22–32. ACM, New York (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, Computer Science and Mathematics, Università degli Studi dell’Aquila, Via Vetoio 2, 67100, L’Aquila, Italy

    Phuong T. Nguyen, Juri Di Rocco & Davide Di Ruscio

Authors
  1. Phuong T. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juri Di Rocco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Davide Di Ruscio
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Phuong T. Nguyen .

Editor information

Editors and Affiliations

  1. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Henderik A. Proper

  2. Stockholm University, Kista, Sweden

    Janis Stirna

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

Nguyen, P.T., Di Rocco, J., Di Ruscio, D. (2019). Building Information Systems Using Collaborative-Filtering Recommendation Techniques. In: Proper, H., Stirna, J. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2019. Lecture Notes in Business Information Processing, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-20948-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20948-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20947-6

  • Online ISBN: 978-3-030-20948-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation