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Research Data Reusability with Content-Based Recommender System

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Deep Learning Theory and Applications (DeLTA 2023)


The use of content-based recommender systems to enable the reusability of research data artifacts has gained significant attention in recent years. This study aims to evaluate the effectiveness of such systems in improving the accessibility and reusability of research data artifacts. The study employs an empirical study to identify content-based recommender systems’ strengths and limitations for recommending research data-collections (repositories). The empirical study involves developing and evaluating a prototype content-based recommender system for research data artifacts. The literature review findings reveal that content-based recommender systems have several strengths, including providing personalized recommendations, reducing information overload, and enhancing retrieved artifacts’ quality, especially when dealing with cold start problems. The results of the empirical study indicate that the developed prototype content-based recommender system effectively provides relevant recommendations for research data repositories. The evaluation of the system using standard evaluation metrics shows that the system achieves an accuracy of 79% in recommending relevant items. Additionally, the user evaluation of the system confirms the relevancy of recommendations and enhances the accessibility and reusability of research data artifacts. In conclusion, the study provides evidence that content-based recommender systems can effectively enable the reusability of research data artifacts.

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  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

  6. 6.


  1. Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2001).

    Chapter  Google Scholar 

  2. Aggarwal, C.C., Yu, P.S.: Outlier detection for high dimensional data. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, pp. 37–46 (2001)

    Google Scholar 

  3. Bennett, D.A.: How can i deal with missing data in my study? Aust. NZ. J. Public Health 25(5), 464–469 (2001)

    Article  Google Scholar 

  4. Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. (CSUR) 53(3), 1–37 (2020)

    Article  Google Scholar 

  5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  6. Färber, M., Leisinger, A.K.: Recommending datasets for scientific problem descriptions. In: CIKM, pp. 3014–3018 (2021)

    Google Scholar 

  7. Heinrichs, B.P.A., Politze, M., Yazdi, M.A.: Evaluation of architectures for FAIR data management in a research data management use case. In: Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022), SCITEPRESS - Science and Technology Publications, Setúbal (2022).

  8. Ho-Dac, N.N., Carson, S.J., Moore, W.L.: The effects of positive and negative online customer reviews: do brand strength and category maturity matter? J. Market. 77(6), 37–53 (2013)

    Article  Google Scholar 

  9. Jones, A.M., Arya, A., Agarwal, P., Gaurav, P., Arya, T.: An ontological sub-matrix factorization based approach for cold-start issue in recommender systems. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pp. 161–166. IEEE (2017)

    Google Scholar 

  10. Kenton, J.D.M.W.C., Toutanova, L.K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of naacL-HLT, pp. 4171–4186 (2019)

    Google Scholar 

  11. Nair, A.M., Benny, O., George, J.: Content based scientific article recommendation system using deep learning technique. In: Suma, V., Chen, J.I.-Z., Baig, Z., Wang, H. (eds.) Inventive Systems and Control. LNNS, vol. 204, pp. 965–977. Springer, Singapore (2021).

    Chapter  Google Scholar 

  12. Phung, S., Kumar, A., Kim, J.: A deep learning technique for imputing missing healthcare data. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6513–6516. IEEE (2019)

    Google Scholar 

  13. Politze, M., Bensberg, S., Müller, M.S.: Managing discipline-specific metadata within an integrated research data management system. In: ICEIS (2), pp. 253–260 (2019)

    Google Scholar 

  14. Revathy, V.R., Anitha, S.P.: Cold start problem in social recommender systems: state-of-the-art review. In: Bhatia, S.K., Tiwari, S., Mishra, K.K., Trivedi, M.C. (eds.) Advances in Computer Communication and Computational Sciences. AISC, vol. 759, pp. 105–115. Springer, Singapore (2019).

    Chapter  Google Scholar 

  15. Rogers, A., Kovaleva, O., Rumshisky, A.: A primer in BERTology: What we know about how BERT works. Trans. Assoc. Comput. Linguist. 8, 842–866 (2021)

    Article  Google Scholar 

  16. Tenopir, C., et al.: Academic librarians and research data services: attitudes and practices. IT Lib: Inf. Technol. Libr. J. Issue 1 (2019)

    Google Scholar 

  17. Tenopir, C., et al.: Data sharing, management, use, and reuse: practices and perceptions of scientists worldwide. PLoS ONE 15(3), e0229003 (2020)

    Article  Google Scholar 

  18. Ünal, Y., Chowdhury, G., Kurbanoğlu, S., Boustany, J., Walton, G.: Research data management and data sharing behaviour of university researchers. In: Proceedings of ISIC: The Information Behaviour Conference, vol. 3, p. 15 (2019)

    Google Scholar 

  19. Vardigan, M., Donakowski, D., Heus, P., Ionescu, S., Rotondo, J.: Creating rich, structured metadata: lessons learned in the metadata portal project. IASSIST Q. 38(3), 15–15 (2015)

    Article  Google Scholar 

  20. Yazdi, M.A.: Enabling operational support in the research data life cycle. In: Proceedings of the First International Conference on Process Mining (ICPM), Doctoral Consortium, pp. 1–10 (2019)

    Google Scholar 

  21. Yazdi, M.A., Politze, M.: Reverse engineering: the university distributed services. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) FTC 2020. AISC, vol. 1289, pp. 223–238. Springer, Cham (2021).

    Chapter  Google Scholar 

  22. Yazdi, M.A., Schimmel, D., Nellesen, M., Politze, M., Müller, M.S.: Da4rdm: data analysis for research data management systems. In: 13th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, (KMIS), pp. 177–183 (2021).

  23. Yazdi, M.A., Ghalati, P.F., Heinrichs, B.: Event log abstraction in client-server applications. In: 13th International Conference on Knowledge Discovery and Information Retrieval (KDIR), pp. 27–36 (2021).

  24. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. (CSUR) 52(1), 1–38 (2019)

    Article  Google Scholar 

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Correspondence to M. Amin Yazdi .

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Yazdi, M.A., Politze, M., Heinrichs, B. (2023). Research Data Reusability with Content-Based Recommender System. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham.

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