Content-Based Recommender System for Plugin Selection in Wordpress-Based Content Management Systems

Content Management Systems (CMSs) play a vital role in modern website development and management. WordPress, Drupal, and Joomla are widely used open-source CMS platforms, with WordPress being the most popular due to its flexibility and ease of use. CMS platforms enable users to create, edit, and manage website content without extensive technical knowledge. This study analyzes the performance of WordPress-based web applications and explores the use of a component-based recommender system for website configuration. Content-based filtering algorithms are applied to recommend suitable implementation components. The results demonstrate the effectiveness of the proposed approach in improving website development and configuration processes. Overall, WordPress provides a reliable and scalable platform for content management

WordPress, Content Management system, Software Product line

1. Addey D, Ellis J, Suh P., Theimecke. D. (2002). Content management system (Tools of the trade) [Google Scholar] [Crossref]

2. Barker, D. (2015). Web Content Management: Systems, Features, and Best Practices (First ed., Vol.1). New York: O'Reilly. Retrieved January 7, 2018, from https://www.oreilly.com/library/view/web- contentmanagement/ 9781491908112/ [Google Scholar] [Crossref]

3. Butkiewicz, M. (2011). Understanding Website Complexity:Measurement, Metrics and Implementation. 10-15. Retrieved January 8, 2018, from https://users.ece.cmu.edu/~vsekar/papers/imc191-butkiewicz.pdf [Google Scholar] [Crossref]

4. Butkiewicz, M., Madhyastha, H. V., & Sekar, V. (2011). Understanding Website Complexity: Measurements, Metrics, and Implications. ACM. [Google Scholar] [Crossref]

5. Bhattacharjee, S. (2011). Evaluation of Content Management Software with Comparison among Joomla,WordPress and Drupal. 17-21. [Google Scholar] [Crossref]

6. M. A. Babar, L. Chen, and F. Shull, ``Managing variability in software product lines,'' IEEE Softw., vol. 27, no. 3, pp. 89_91, May/Jun. 2010. [Google Scholar] [Crossref]

7. M. Lettner, J. Rodas, J. A. Galindo, and D. Benavides, ``Automated analysis of two-layered feature models with feature attributes,'' J. Comput. Lang., vol. 51, pp. 154_172, Apr. 2019. [Google Scholar] [Crossref]

8. K. C. Kang, S. G. Cohen, J. A. Hess, W. E. Novak, and A. S. Peterson, ``Feature-oriented domain analysis (FODA) feasibility study,'' DTIC, Softw. Eng. Int., Pittsburgh, PA, USA, Tech. Rep. CMU/SEI-90-TR-021,1990. [Google Scholar] [Crossref]

9. J. A. Pereira, J. Martinez, H. K. Gurudu, S. Krieter, and G. Saake, ``Visual guidance for product line con_guration using recommendations and nonfunctionalproperties,'' in Proc. 33rd Annu. Symp. Appl. Comput. (SAC),Pau, France, Apr. 2018, pp. 2058_2065. [Google Scholar] [Crossref]

10. J. A. Pereira, S. Schulze, S. Krieter, M. Ribeiro, and G. Saake, ``A contextaware recommender system for extended software product line con_gurations,''in Proc. 12th Int. Workshop Variability Modelling Softw.-Intensive Syst., 2018, pp. 97_104. [Google Scholar] [Crossref]

11. J. A. Pereira, S. Schulze, E. Figueiredo, and G. Saake, ``N-dimensional tensor factorization for self-con_guration of software product lines at runtime,'' in Proc. 22nd Int. Syst. Softw. Product Line Conf. (SPLC), New York, NY, USA, vol. 1, 2018, pp. 87_97. [Online]. Available:http://doi.acm.org/10.1145/3233027.3233039. [Google Scholar] [Crossref]

12. P. Clements and L. Northrop, Software Product Lines: Practices and Patterns (The SEI series in software engineering). Boston, MA, USA: Addison-Wesley, 2001. [Google Scholar] [Crossref]

13. J. A. Pereira, K. Constantino, and E. Figueiredo, ``A systematic literature review of softwareproduct line management tools,'' in Proc. Int. Conf. Softw. Reuse. Cham, Switzerland: Springer, 2015, pp. 73_89. [Google Scholar] [Crossref]

14. G. K. Narwane, J. A. Galindo, S. N. Krishna, D. Benavides, J.-V. Millo, and S. Ramesh, ``Traceability analyses between features and assets in software product lines,'' Entropy, vol. 18, no. 8, p. 269, 2016 [Google Scholar] [Crossref]

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