Skip to main content

Analyzing Performances of Three Context-Aware Collaborator Recommendation Algorithms in Terms of Accuracy and Time Efficiency

  • Conference paper
  • First Online:
Information and Knowledge Systems. Digital Technologies, Artificial Intelligence and Decision Making (ICIKS 2021)

Abstract

Nowadays, more and more collaborative tools are available to support users’ remote collaborations. Its increasing amount makes users struggle in managing and retrieving information about their collaborators during collaboration. To solve this problem, many decision support systems have been developed quickly, such as recommender systems and context-aware recommender systems. However, the performances of different algorithms in such systems are relatively unexplored. Based on our three proposed context-aware collaborator recommendation algorithms (i.e., PreF1, PoF1, and PoF2), we are interested in analyzing and evaluating their performances in terms of accuracy and time efficiency. The three algorithms all process the context of collaboration by means of ontology-based semantic similarity, but employ the similarity following two approaches respectively, to generate context-aware collaborator recommendations. In this paper, we present how to test, analyze, and evaluate the performances of the three context-aware collaborator algorithms in terms of accuracy and time efficiency.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Such a RS is also mentioned as 2D RS in the rest of this paper.

  2. 2.

    Fig. 1 also illustrates how \(D_{1}, D_{2}, ..., D_{n}\) are utilized in different methods.

  3. 3.

    The detailed equations of \(S^{i}_{1} (d,c)\) and \(S^{j}_{2} (d,c)\) are available in [13]. This paper do not discuss how to calculate \(S^{i}_{1} (d,c)\) and \(S^{j}_{2} (d,c)\).

  4. 4.

    This dataset can downloaded from https://www.aminer.org/citation.

  5. 5.

    These domains are Art, Biology, Business, Chemistry, Computer science, Economics, Engineering, Environmental science, Geography, Geology, History, Materials science, Mathematics, Medicine, Philosophy, Physics, Political science, Psychology, Sociology, and Others.

  6. 6.

    Here, c is a testing collaboration; |X| represents the number of training collaborations; \(d (d \in X, d \ne c)\) is a training collaboration.

  7. 7.

    The range of F1 is [0, 1].

  8. 8.

    The range of MAE is \([0,+\infty )\).

  9. 9.

    In our experiments, execution time is counted in milliseconds.

  10. 10.

    Here, IC represents \(IC(c) = -\log p(c)\), where p(c) is the probability of c’s appearance in an ontology [22].

References

  1. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. (TOIS) 23(1), 103–145 (2005)

    Article  Google Scholar 

  2. 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 

  3. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_7

    Chapter  Google Scholar 

  4. Bridge, D., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. Knowl. Eng. Rev. 20(3), 315–320 (2005)

    Article  Google Scholar 

  5. De Gemmis, M., Lops, P., Semeraro, G., Musto, C.: An investigation on the serendipity problem in recommender systems. Inf. Process. Manag. 51(5), 695–717 (2015)

    Article  Google Scholar 

  6. 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 (2011). https://doi.org/10.1007/978-0-387-85820-3_4

    Chapter  Google Scholar 

  7. Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: Developing constraint-based recommenders. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 187–215. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_6

    Chapter  Google Scholar 

  8. Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31865-1_25

    Chapter  Google Scholar 

  9. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)

    Google Scholar 

  10. Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 337–344 (2004)

    Google Scholar 

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

    Chapter  Google Scholar 

  12. Li, S., Abel, M.H., Negre, E.: Towards a collaboration context ontology. In: 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 93–98. IEEE (2019)

    Google Scholar 

  13. Li, S., Abel, M.H., Negre, E.: Ontology-based semantic similarity in generating context-aware collaborator recommendations. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 751–756. IEEE (2021)

    Google Scholar 

  14. Liu, Z., Xie, X., Chen, L.: Context-aware academic collaborator recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1870–1879. ACM (2018)

    Google Scholar 

  15. 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, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_3

    Chapter  Google Scholar 

  16. Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 54–88 (2004)

    Article  Google Scholar 

  17. Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)

    Google Scholar 

  18. Nunes, I., Jannach, D.: A systematic review and taxonomy of explanations in decision support and recommender systems. User Model. User-Adap. Inter. 27(3–5), 393–444 (2017). https://doi.org/10.1007/s11257-017-9195-0

    Article  Google Scholar 

  19. Van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Advances in Neural Information Processing Systems, pp. 2643–2651 (2013)

    Google Scholar 

  20. Palmisano, C., Tuzhilin, A., Gorgoglione, M.: Using context to improve predictive modeling of customers in personalization applications. IEEE Trans. Knowl. Data Eng. 20(11), 1535–1549 (2008)

    Article  Google Scholar 

  21. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_1

    Chapter  MATH  Google Scholar 

  22. Sánchez, D., Batet, M., Isern, D., Valls, A.: Ontology-based semantic similarity: a new feature-based approach. Expert Syst. Appl. 39(9), 7718–7728 (2012)

    Article  Google Scholar 

  23. 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, pp. 285–295 (2001)

    Google Scholar 

  24. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: KDD’08, pp. 990–998 (2008)

    Google Scholar 

  25. Tarus, J.K., Niu, Z., Mustafa, G.: Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artif. Intell. Rev. 50(1), 21–48 (2018). https://doi.org/10.1007/s10462-017-9539-5

    Article  Google Scholar 

  26. Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30(1), 79–82 (2005)

    Article  Google Scholar 

  27. Xu, Y., Hao, J., Lau, R.Y., Ma, J., Xu, W., Zhao, D.: A personalized researcher recommendation approach in academic contexts: Combining social networks and semantic concepts analysis. In: PACIS, p. 144 (2010)

    Google Scholar 

  28. Zhang, Z., Gong, L., Xie, J.: Ontology-based collaborative filtering recommendation algorithm. In: Liu, D., Alippi, C., Zhao, D., Hussain, A. (eds.) BICS 2013. LNCS (LNAI), vol. 7888, pp. 172–181. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38786-9_20

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siying Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, S., Abel, MH., Negre, E. (2021). Analyzing Performances of Three Context-Aware Collaborator Recommendation Algorithms in Terms of Accuracy and Time Efficiency. In: Saad, I., Rosenthal-Sabroux, C., Gargouri, F., Arduin, PE. (eds) Information and Knowledge Systems. Digital Technologies, Artificial Intelligence and Decision Making. ICIKS 2021. Lecture Notes in Business Information Processing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-030-85977-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85977-0_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85976-3

  • Online ISBN: 978-3-030-85977-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics