Requirements engineering is a critical phase within the software development lifecycle, with its effective execution being pivotal to the overall success of a project. To improve the quality and efficiency of requirements engineering, the reuse of previous requirements has emerged as a promising strategy. By employing methodologies such as collaborative filtering, content-based filtering, knowledge-based techniques, and hybrid approaches, recommender systems support stakeholders in the precise identification and prioritization of requirements. This paper presents a hybrid method combining collaborative filtering and content-based filtering to improve the performance of recommendation systems in requirements engineering. In this method, the semantic similarity between requirements is calculated using the Global Vectors for Word Representation (GloVe) model, and the semantic features extracted from textual descriptions are combined with predefined categories. This process enhances the accuracy of predicting stakeholder interest or need for requirements and their prioritization. The experimental results obtained from the RALIC dataset indicate that the proposed approach significantly enhances prediction accuracy and coverage. This improvement is particularly evident in addressing challenges related to data sparsity and the cold start problem, highlighting the superior efficiency of this method compared to existing approaches.
Pourhashem Kallehbasti, M. M. , Pirgazi, J. , Ghanbari Sorkhi, A. and Kermani, A. (2024). Enhancing Requirements Engineering Process using Hybrid Recommender Systems. Soft Computing Journal, (), -. doi: 10.22052/scj.2025.255465.1267
MLA
Pourhashem Kallehbasti, M. M. , , Pirgazi, J. , , Ghanbari Sorkhi, A. , and Kermani, A. . "Enhancing Requirements Engineering Process using Hybrid Recommender Systems", Soft Computing Journal, , , 2024, -. doi: 10.22052/scj.2025.255465.1267
HARVARD
Pourhashem Kallehbasti, M. M., Pirgazi, J., Ghanbari Sorkhi, A., Kermani, A. (2024). 'Enhancing Requirements Engineering Process using Hybrid Recommender Systems', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2025.255465.1267
CHICAGO
M. M. Pourhashem Kallehbasti , J. Pirgazi , A. Ghanbari Sorkhi and A. Kermani, "Enhancing Requirements Engineering Process using Hybrid Recommender Systems," Soft Computing Journal, (2024): -, doi: 10.22052/scj.2025.255465.1267
VANCOUVER
Pourhashem Kallehbasti, M. M., Pirgazi, J., Ghanbari Sorkhi, A., Kermani, A. Enhancing Requirements Engineering Process using Hybrid Recommender Systems. Soft Computing Journal, 2024; (): -. doi: 10.22052/scj.2025.255465.1267
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