The week's pick
Random Articles
Reseach Article
Prescriptive Analytics for Publication Venue Recommendation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 7 |
| Year of Publication: 2024 |
| Authors: Tolulope Anthonia Adebayo, Bolanle Adefowoke Ojokoh |
10.5120/ijca2024923397
|
Tolulope Anthonia Adebayo, Bolanle Adefowoke Ojokoh . Prescriptive Analytics for Publication Venue Recommendation. International Journal of Computer Applications. 186, 7 ( Feb 2024), 11-17. DOI=10.5120/ijca2024923397
Abstract
Publication venue recommendation provide answers to one of the major challenges researchers face while seeking to get their research results or findings published in high-valued journals and conferences for easy dissemination and to maximize effects on future research. However, most recommendation systems available use traditional approaches which encounter problems such as cold start, data sparsity, among others. Hence, this study proposes a two-level recommendation model using prescriptive analytics technique (fuzzy logic) algorithm to infer decision on a suitable venue for publication based on key parameters such as the cost of publishing, impact factor of a journal or rank of a conference, and the average duration of review. Experiments were carried out on real world dataset obtained from Digital Bibliography and Library Project (DBLP) and Aminer Digital repositories. Results obtained from the evaluation of the system in terms of Accuracy@N, Precision, and F1-measure shows that the system performed efficiently and provides effective recommendation.
References
- Yang, Z. and Davison, B. (2012). Venue recommendation: Submitting your paper with style. In 11th International Conference on Machine Learning and Applications, 1, 681-686.
- Cai, H. (2016). Publication venue recommendation in heterogeneous information networks (Doctoral dissertation). University of Illinois at Urbana-Champaign, Urbana, Illinois.
- Isinkaye, F., Folajimi, Y. and Ojokoh B. (2015). Recommendation systems: Principles, Methods and Evaluation. Egyptian Informatics Journal, 16, 261-273.
- Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X. and Xia, F. (2019). Scientific Paper Recommendation: A Survey. IEEE Access, 7, 9324-9339.
- Lepenioti, K., Bousdekis, A., Apostolou, D. and Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70.
- Frazzetto, D., Nielsen, T., Pedersen, T. and Siksnys, L. (2019). Prescriptive Analytics: A Survey of Emerging Trends and Technologies. VLDB Journal, 28(4), 575-595.
- Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A. and Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. applied sciences, 10(21), 7748.
- Ojokoh, B., Omisore, M., Samuel, O. and Ogunniyi, T. (2012). A fuzzy logic based personalized recommender system. International Journal of Computer Science and Information Technology & Security (IJCSITS), 2(5), 1008-1015.
- Alhoori, H. and Furuta, R. (2017). Recommendation of Scholarly Venues Based on Dynamic User Interests. Journal of Informetrics, 11(2), 553-563.
- Luong, H., Huynh, T., Gauch, S., Do P. and Hoang, K. (2012). Publication venue recommendation using author network's publication history. In Proceedings on the 4th Asian Conference on Intelligent Information and Database Systems, 3, 426–435.
- Boukhris, I. and Ayachi, R. (2014). A novel personalized academic venue hybrid recommender. In IEEE 15th International Symposium on Computational Intelligence and Informatics, (pp. 465-470).
- Medvet, E., Bartoli, A. and Piccinin, G. (2014). Publication venue recommendation based on paper abstract. In: Proceedings of the IEEE 26th International Conference on Tools with Artificial Intelligence, (pp. 10-12).
- Kang, N., Doornenbal, M. and Schijvenaars, B. (2015). Elsevier Journal Finder: Recommending Journals for your Paper. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 261-264).
- Feng, X., Zhang, H., Ren, Y., Shang, P., Zhu, Y., Liang, Y., Guan, R. and Xu, D. (2019). The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study. International Journal of Medical Internet Research, 21(5), e12957.
- Wang, D., Liang, Y., Xu, D., Feng, X. and Guan, R. (2018). A content-based recommender system for computer science publications. Journal of Knowledge-Based Systems,157,1-9. Pradhan, T., Gupta, A. and Pal, S. (2020). HASVRec: A modularized Hierarchical Attention-based Scholarly Venue Recommender system. Knowledge-Based Systems, 204,106181
Index Terms
Keywords