{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:29:09Z","timestamp":1784215749721,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This research aims to enhance e-recruitment systems using text summarization techniques and pretrained large language models (LLMs). A job recommender system is built with integrated text summarization. The text summarization techniques that are selected are BART, T5 (Text-to-Text Transfer Transformer), BERT, and Pegasus. Content-based recommendation is the model chosen to be implemented. The LinkedIn Job Postings dataset is used. The evaluation of the text summarization techniques is performed using ROUGE-1, ROUGE-2, and ROUGE-L. The results of this approach deduce that the recommendation does improve after text summarization. BERT outperforms other summarization techniques. Recommendation evaluations show that, for MRR, BERT performs 256.44% better, indicating relevant recommendations at the top more effectively. For RMSE, there is a 29.29% boost, indicating recommendations closer to the actual values. For MAP, a 106.46% enhancement is achieved, presenting the highest precision in recommendations. Lastly, for NDCG, there is an 83.94% increase, signifying that the most relevant recommendations are ranked higher.<\/jats:p>","DOI":"10.3390\/info16040333","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T20:38:26Z","timestamp":1745267906000},"page":"333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing E-Recruitment Recommendations Through Text Summarization Techniques"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5848-3912","authenticated-orcid":false,"given":"Reham Hesham","family":"El-Deeb","sequence":"first","affiliation":[{"name":"College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria P.O. Box 1029, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8726-605X","authenticated-orcid":false,"given":"Walid","family":"Abdelmoez","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria P.O. Box 1029, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6553-4159","authenticated-orcid":false,"given":"Nashwa","family":"El-Bendary","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Aswan P.O. Box 11, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3407190","article-title":"Recommender Systems Leveraging Multimedia Content","volume":"53","author":"Deldjoo","year":"2020","journal-title":"Acm Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100255","DOI":"10.1016\/j.cosrev.2020.100255","article-title":"Context Aware Recommendation Systems: A review of the state of the art techniques","volume":"37","author":"Kulkarni","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s10462-019-09684-w","article-title":"A study on features of social recommender systems","volume":"53","author":"Shokeen","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102310","DOI":"10.1016\/j.ipm.2020.102310","article-title":"A collaborative filtering recommender system using genetic algorithm","volume":"57","author":"Alhijawi","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.3233\/IDA-163209","article-title":"Hybrid recommender systems: A systematic literature review","volume":"21","author":"Morisio","year":"2017","journal-title":"Intell. Data Anal."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2016). Recommender Systems, Springer.","DOI":"10.1007\/978-3-319-29659-3"},{"key":"ref_7","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2023). Attention Is All You Need. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3666003","article-title":"MizBERT: A Mizo BERT Model","volume":"23","author":"Lalramhluna","year":"2024","journal-title":"Acm Trans. Asian-Low-Resour. Lang. Inf. Process."},{"key":"ref_9","first-page":"8363","article-title":"Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks","volume":"38","author":"Du","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Patil, A., Suwalka, D., Kumar, A., Rai, G., and Saha, J. (2023, January 23\u201325). A Survey on Artificial Intelligence (AI) based Job Recommendation Systems. Proceedings of the 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India.","DOI":"10.1109\/ICSCDS56580.2023.10104718"},{"key":"ref_11","unstructured":"Ghosh, P., and Sadaphal, V. (2023). JobRecoGPT\u2014Explainable job recommendations using LLMs. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gadegaonkar, S., Lakhwani, D., Marwaha, S., and Salunke, P.A. (2023, January 2\u20134). Job Recommendation System using Machine Learning. Proceedings of the 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India.","DOI":"10.1109\/ICAIS56108.2023.10073757"},{"key":"ref_13","unstructured":"Denis, R., Peter Jose, P., and Sushma Margaret, A. (2023, January 15\u201317). Performance Analysis of Machine Learning\u2014Semantic Relational Approach based Job Recommendation System. Proceedings of the 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alsaif, S.A., Sassi Hidri, M., Ferjani, I., Eleraky, H.A., and Hidri, A. (2022). NLP-Based Bi-Directional Recommendation System: Towards Recommending Jobs to Job Seekers and Resumes to Recruiters. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6040147"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, M., Zhu, Y., Lv, N., and He, R. (2022, January 14\u201316). A Feature Fusion-based Representation Learning Model for Job Recommendation. Proceedings of the 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China.","DOI":"10.1109\/ICCECE54139.2022.9712756"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Minhas, A.H., Shaiq, M.D., Qureshi, S.A., Cheema, M.D.A., Hussain, S., and Khan, K.U. (2022, January 3\u20135). An Efficient Algorithm for Ranking Candidates in E-Recruitment System. Proceedings of the 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), Seoul, Republic of Korea.","DOI":"10.1109\/IMCOM53663.2022.9721629"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, G. (2022, January 14\u201316). Human Resource Recommendation Based on Recurrent Convolutional Neural Network. Proceedings of the 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China.","DOI":"10.1109\/ICCECE54139.2022.9712799"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Puspasari, B.D., Damayanti, L.L., Pramono, A., and Darmawan, A.K. (2021, January 2). Implementation K-Means Clustering Method in Job Recommendation System. Proceedings of the 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Malang, Indonesia.","DOI":"10.1109\/ICEEIE52663.2021.9616654"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Allouache, Y., and Joubert, C. (2021, January 6\u20139). A Staffing Recommender System based on Domain-Specific Knowledge Graph. Proceedings of the 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS), Gandia, Spain.","DOI":"10.1109\/SNAMS53716.2021.9732087"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Saeed, T., Sufian, M., Ali, M., and Rehman, A.U. (2021, January 9\u201310). Convolutional Neural Network Based Career Recommender System for Pakistani Engineering Students. Proceedings of the 2021 International Conference on Innovative Computing (ICIC), Lahore, Pakistan.","DOI":"10.1109\/ICIC53490.2021.9715788"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rafiei, G., Farahani, B., and Kamandi, A. (2021, January 1\u20132). Towards Automating the Human Resource Recruiting Process. Proceedings of the 2021 5th National Conference on Advances in Enterprise Architecture (NCAEA), Mashhad, Iran.","DOI":"10.1109\/NCAEA54556.2021.9690504"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, J., Viaud, G., and Hudelot, C. (2021, January 13\u201316). Improving Next-Application Prediction with Deep Personalized-Attention Neural Network. Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Pasadena, CA, USA.","DOI":"10.1109\/ICMLA52953.2021.00258"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bellini, V., Biancofiore, G.M., Di Noia, T., Sciascio, E.D., Narducci, F., and Pomo, C. (2020, January 27\u201329). GUapp: A Conversational Agent for Job Recommendation for the Italian Public Administration. Proceedings of the 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), Bari, Italy.","DOI":"10.1109\/EAIS48028.2020.9122756"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yadalam, T.V., Gowda, V.M., Kumar, V.S., Girish, D., and Namratha, M. (2020, January 10\u201312). Career Recommendation Systems using Content based Filtering. Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.","DOI":"10.1109\/ICCES48766.2020.9137992"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nigam, A., Roy, A., Singh, H., and Waila, H. (2019, January 19\u201321). Job Recommendation through Progression of Job Selection. Proceedings of the 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS), Singapore.","DOI":"10.1109\/CCIS48116.2019.9073723"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jain, H., and Kakkar, M. (2019, January 10\u201311). Job Recommendation System based on Machine Learning and Data Mining Techniques using RESTful API and Android IDE. Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.","DOI":"10.1109\/CONFLUENCE.2019.8776964"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Liao, F., Ge, L., and Sun, J. (2019, January 19\u201323). Personalized Preference Collaborative Filtering: Job Recommendation for Graduates. Proceedings of the 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Leicester, UK.","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00203"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mehta, M., Derasari, R., Patel, S., Kakadiya, A., Gandhi, R., Chaudhary, S., and Goswami, R. (2019, January 8\u201311). A Service-Oriented Human Capital Management Recommendation Platform. Proceedings of the 2019 IEEE International Systems Conference (SysCon), Orlando, FL, USA.","DOI":"10.1109\/SYSCON.2019.8836842"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, Y., Huang, Y., and Chen, P. (2019, January 17\u201319). Employment Recommendation Algorithm Based on Ensemble Learning. Proceedings of the 2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Kunming, China.","DOI":"10.1109\/ICCASIT48058.2019.8973135"},{"key":"ref_30","first-page":"280","article-title":"BERT-based Job Recommendation System Using LinkedIn Dataset","volume":"10","author":"Almalki","year":"2025","journal-title":"J. Inf. Syst. Eng. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"04022060","DOI":"10.1061\/(ASCE)ME.1943-5479.0001087","article-title":"Use of LinkedIn Data and Machine Learning to Analyze Gender Differences in Construction Career Paths","volume":"38","author":"Hickey","year":"2022","journal-title":"J. Manag. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Panchasara, S., Gupta, R.K., and Sharma, A. (2023, January 18\u201319). AI Based Job Recommedation System using BERT. Proceedings of the 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India.","DOI":"10.1109\/ICCUBEA58933.2023.10392119"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3250","DOI":"10.1109\/TIT.2004.838101","article-title":"The Similarity Metric","volume":"50","author":"Li","year":"2004","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lahitani, A.R., Permanasari, A.E., and Setiawan, N.A. (2016, January 26\u201327). Cosine similarity to determine similarity measure: Study case in online essay assessment. Proceedings of the 2016 4th International Conference on Cyber and IT Service Management, Bandung, Indonesia.","DOI":"10.1109\/CITSM.2016.7577578"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rosnes, D., Starke, A.D., and Trattner, C. (2024, January 1\u20134). Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity Metrics. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, Cagliari, Italy.","DOI":"10.1145\/3627043.3659560"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1023\/A:1026501525781","article-title":"User Modeling for Adaptive News Access","volume":"10","author":"Billsus","year":"2000","journal-title":"User Model.-User-Adapt. Interact."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"26","DOI":"10.59796\/jcst.V14N2.2024.26","article-title":"Comparative Study on Automated Reference Summary Generation using BERT Models and ROUGE Score Assessment","volume":"14","author":"Sanchan","year":"2024","journal-title":"J. Curr. Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"128255","DOI":"10.1016\/j.neucom.2024.128255","article-title":"Abstractive Text Summarization: State of the Art, Challenges, and Improvements","volume":"603","author":"Shakil","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_39","first-page":"352","article-title":"Comparing BERT Against Traditional Machine Learning Models in Text Classification","volume":"2","year":"2023","journal-title":"J. Comput. Cogn. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wehnert, S., Sudhi, V., Dureja, S., Kutty, L., Shahania, S., and De Luca, E.W. (2021, January 21\u201325). Legal norm retrieval with variations of the bert model combined with TF-IDF vectorization. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, S\u00e3o Paulo, Brazil.","DOI":"10.1145\/3462757.3466104"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/333\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:18:51Z","timestamp":1760030331000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,21]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["info16040333"],"URL":"https:\/\/doi.org\/10.3390\/info16040333","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,21]]}}}