Job and Candidate Recommendation with Big Data Support: A Contextual Online Learning Approach

Dong, Shaokang; Lei, Zijian; Zhou, Pan; Bian, Kaigui; Liu, Guanghui

doi:10.1109/glocom.2017.8255006

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Finally, several tests are carried out to evaluate our algorithm based on a huge database from Work4, which is the worldwide leader in social and mobile recruitment. These experiments may demonstrate that our algorithm has remarkable performance in comparison to other algorithms that already exist [16].…”

Section: Literature Studiesmentioning

confidence: 66%

A Review of System for Recommending Jobs Using Machine Learning

Y¹,

T²

2023

IJSREM

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The purpose of this research is to conduct a literature assessment of job recommender systems (JRS) that have been published in the recent past. When compared to our prior evaluations of the relevant literature, we placed a greater amount of importance on contributions that took into account the temporal and reciprocal aspects of job recommendations. Previous research on JRS has shown that it may be possible to enhance model performance by taking different perspectives like these into consideration when designing JRS. Additionally, it may result in a more balanced distribution of applicants among a group of occupations that are comparable to one another. In addition to this, we look at the literature from the point of view of the fairness of algorithms. When we looked into this, we discovered that this topic is seldom brought up in the academic literature, and when it does, many writers make the incorrect assumption that deleting the discriminating characteristic would be enough. When referring to the kinds of models that are used in JRS, writers usually refer to their approach as being "hybrid." In doing so, however, they unfortunately conceal what exactly these procedures include. We divided this expansive class of hybrids into more manageable subclasses by making use of the recommender taxonomies that were already in existence. In addition, we come to the conclusion that the availability of data, and more specifically the availability of click data, has a significant bearing on the selection of a validation technique. Last but not least, despite the fact that the generalizability of JRS across various datasets is seldom taken into consideration, the findings imply that error scores may change across these datasets. Keyword: Job Recommender Systems, Machine Learning , Businesses , Content Based Filtering , Gradient Boosting Regression Tree.

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Section: Literature Studiesmentioning

confidence: 66%

A Review of System for Recommending Jobs Using Machine Learning

Y¹,

T²

2023

IJSREM

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“…Three contributions model the job recommendation problem with a somewhat different objective function, though, we still label these as MM-SE. Dong et al [38] and subsequent work [26] propose an MM-SE monolithic hybrid. Contrary to the approaches discussed so far, they consider the problem as a reinforcement learning problem.…”

Section: Model-based Methods On Shallow Embeddingsmentioning

confidence: 99%

Job Recommender Systems: A Review

Ruijt¹,

Bhulai²

2021

Preprint

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This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021). Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations. Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance. Also, it may lead to a more uniform distribution of candidates over a set of similar jobs. We also consider the literature from the perspective of algorithm fairness. Here we find that this is rarely discussed in the literature, and if it is discussed, many authors wrongly assume that removing the discriminatory feature would be sufficient. With respect to the type of models used in JRS, authors frequently label their method as 'hybrid'. Unfortunately, they thereby obscure what these methods entail. Using existing recommender taxonomies, we split this large class of hybrids into subcategories that are easier to analyse. We further find that data availability, and in particular the availability of click data, has a large impact on the choice of method and validation. Last, although the generalizability of JRS across different datasets is infrequently considered, results suggest that error scores may vary across these datasets.

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“…In 'All Questions' section all the questions raised by various users will be visible as shown in Figure 11. If the user wants, they can provide answers to those questions [14].…”

Section: Figure 11 All Questionsmentioning

confidence: 99%

Innovative Approach for Career Using Career Roadmap Provider

Ayush Dharmare,

Aryan Ramedwar,

Kartik Bhujade

et al. 2024

Int Res J Adv Engg Mgt

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In today’s world a lot of people are a bit confused about the field which they need to choose as their career, and even if some people are clear about their goals, they don’t have an actual path which they need to follow to become a professional in their field. There are several articles available on the internet which will provide you information regarding your career but it is very vast and the user interface is not much attractive and precise for the users. Along with that several new fields are emerging in the world around us about which people are not much aware and even the search engines don’t have much information about it. Hence, we are developing a “Career Roadmap Provider” which is a website that will provide our users a detailed roadmap regarding the particular field which they have searched for. The roadmap will include the field description, different available opportunities within that field, the companies that the user can apply for, required skills and other necessary information. This will clear the confusion that several users are having regarding their particular field by providing them a complete and precise Career Roadmap.

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Job and Candidate Recommendation with Big Data Support: A Contextual Online Learning Approach

Cited by 7 publications

References 7 publications

A Review of System for Recommending Jobs Using Machine Learning

A Review of System for Recommending Jobs Using Machine Learning

Job Recommender Systems: A Review

Innovative Approach for Career Using Career Roadmap Provider

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