Enhanced DSSM (deep semantic structure modelling) technique for job recommendation

Mishra, Ravita; Rathi, Sheetal

doi:10.1016/j.jksuci.2021.07.018

Cited by 13 publications

(11 citation statements)

References 28 publications

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“…For example, an ontology job recommender system was proposed in [33] to model areas of knowledge representing the professional skills of every job. A semantic structure model recommender system to define required skill every job information using character trigram format can increase the efficiency of the system has been proposed by [23]. Information technology (IT) skill classification has been found to be a suitable career recommendation for IT students.…”

Section: Related Workmentioning

confidence: 99%

Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia

Siswipraptini,

Warnars,

Ramadhan

et al. 2024

IEEE Access

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One of the challenging decisions for students is taking a job specialization. To make their decisions, they use subjective perceptions of friends or family due to the lack of guidance and limited resources. This increases the risk of dissatisfaction with the work environments. To address these drawbacks, this study presents a personalized career-path recommendation model (CPRM) to provide guidance and help college students choose information technology jobs. The design of the CPRM is based on the personalized Naïve Bayes (p-NB) algorithm with three primary sources: job profiles, personality types, and subjects. The association between personality type and college students was established using samples of 104 computer science students enrolled in private universities in Indonesia. CPRM was implemented as a web-based application. This study evaluated the model by measuring the quality of the recommended items to determine whether the proposed model is well accepted by users. The model considers educational data mining grounded theory (EDM-GT) data integration and hierarchically related concepts. CPRM has been validated by Information Technology (IT) professionals and three psychologists in Indonesia through focus group discussions. The evaluation results showed that more than 83% of respondents were satisfied with the recommendation model. Hence, CPRM can provide automatic academic advisors and guidance to computer science students interested in pursuing careers in IT jobs. The result shows that CPRM is the first career path recommendation model based on EDM-GT to target the computer science community in Indonesia.INDEX TERMS Educational data mining, grounded theory, information technology, naïve bayes, career path recommendation model.

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Section: Related Workmentioning

confidence: 99%

Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia

Siswipraptini,

Warnars,

Ramadhan

et al. 2024

IEEE Access

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“…As one of the fundamental problems in the field of NLP, semantic matching is widely used in downstream tasks such as information retrieval, recommender systems and question and answer systems [ 37 , 53 – 55 ].…”

Section: Related Workmentioning

confidence: 99%

“…Representation-based models emphasizes the construction of the representation layer, encoding the text into overall embedding tensors before matching them, led by Microsoft's DSSM [ 36 ]. A series of models such as CDSSM [ 56 ], LDR-LTM [ 57 ] and Enhanced-DSSM [ 37 ] have since emerged, which have similar structures to DSSM, but replace the expression or matching layer with a more complex and effective algorithm. In retrieval and recommendation tasks, representation-based models can pre-process text with trained sentence embedding models to build indexes and significantly reduce online computation time.…”

Section: Related Workmentioning

confidence: 99%

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Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval

Ding,

Qiao,

Zhang

2023

Plant Methods

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Background In the era of Agri 4.0 and the popularity of Plantwise systems, the availability of Plant Electronic Medical Records has provided opportunities to extract valuable disease information and treatment knowledge. However, developing an effective prescription recommendation method based on these records presents unique challenges, such as inadequate labeling data, lack of structural and linguistic specifications, incorporation of new prescriptions, and consideration of multiple factors in practical situations. Results This study proposes a plant disease prescription recommendation method called PRSER, which is based on sentence embedding retrieval. The semantic matching model is created using a pre-trained language model and a sentence embedding method with contrast learning ideas, and the constructed prescription reference database is retrieved for optimal prescription recommendations. A multi-vegetable disease dataset and a multi-fruit disease dataset are constructed to compare three pre-trained language models, four pooling types, and two loss functions. The PRSER model achieves the best semantic matching performance by combining MacBERT, CoSENT, and CLS pooling, resulting in a Pearson coefficient of 86.34% and a Spearman coefficient of 77.67%. The prescription recommendation capability of the model is also verified. PRSER performs well in closed-set testing with Top-1/Top-3/Top-5 accuracy of 88.20%/96.07%/97.70%; and slightly worse in open-set testing with Top-1/Top-3/Top-5 accuracy of 82.04%/91.50%/94.90%. Finally, a plant disease prescription recommendation system for mobile terminals is constructed and its generalization ability with incomplete inputs is verified. When only symptom information is available without environment and plant information, our model shows slightly lower accuracy with Top-1/Top-3/Top-5 accuracy of 75.24%/88.35%/91.99% in closed-set testing and Top-1/Top-3/Top-5 accuracy of 75.08%/87.54%/89.84% in open-set testing. Conclusions The experiments validate the effectiveness and generalization ability of the proposed approach for recommending plant disease prescriptions. This research has significant potential to facilitate the implementation of artificial intelligence in plant disease treatment, addressing the needs of farmers and advancing scientific plant disease management.

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“…They capture their features including expectations, job history, and skills in an ontology to facilitate matching. In [12], they addressed the dynamic nature of the job market leads to cold start and scalability issues and they proposed a deep semantic structure algorithm that overcomes the issue of the existing system.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Matched Representation System for Job Recommendation

et al. 2022

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Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boards that aggregates postings from many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been implemented, most of them failed to recommend job vacancies that fit properly to the job seekers profiles when dealing with more than one job offer. They consider skills as passive entities associated with the job description, which need to be matched for finding the best job recommendation. This paper provides a recommender system to assist job seekers in finding suitable jobs based on their resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and measuring similarity between the job seeker’s skills and explicit features of job listing using content-based filtering. First-hand information was gathered by scraping jobs description from Indeed from major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job offers were analyzed and job recommendation was made by matching skills from resumes to posted jobs. To quantify recommendation success and error rates, we sought to compare the results of our system to reality using decision support measures.

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Enhanced DSSM (deep semantic structure modelling) technique for job recommendation

Cited by 13 publications

References 28 publications

Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia

Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia

Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval

Learning-Based Matched Representation System for Job Recommendation

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