Skills2Job: A recommender system that encodes job offer embeddings on graph databases

Giabelli, Anna; Malandri, Lorenzo; Mercorio, Fabio; Mezzanzanica, Mario; Seveso, Andrea

doi:10.1016/j.asoc.2020.107049

Cited by 48 publications

(13 citation statements)

References 23 publications

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“…As the length of the recommendation outcomes varies based on input parameters, the NDCG is calculated by normalizing the cumulative gain at every item’s recommendation position [ 113 ]: where IDCG@k denotes the Ideal DCG when the system recommends the most relevant items first: The real challenge with NDCG is that, when only partial relevance feedback is available, we typically do not know the ideal ordering of results. The NDCG, on the other hand, has been demonstrated to be a valuable statistic for measuring the quality of ranking for a range of problems, including job offer [ 114 ], BBC news [ 115 ], and Airbnb bookings [ 116 ] recommendations. We observed the NDCG@5 score highest among the three evaluation parameters.…”

Section: Resultsmentioning

confidence: 99%

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

et al. 2022

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Autism spectrum is a brain development condition that impairs an individual’s capacity to communicate socially and manifests through strict routines and obsessive–compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81–84%, with a normalized discounted cumulative gain of 79–81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models’ treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.

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Section: Resultsmentioning

confidence: 99%

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

et al. 2022

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“…In [132], the authors built a job recommender that jointly learns the representation of the jobs and skills in the shared k-dimensional latent space of job transition network, job-skill network, and skill co-occurrence network. In a similar way, the authors of [133] proposed a recommender system that, starting from a set of users' skills, identifies the most suitable jobs as they emerge from a large dataset of online IT job ads, which were processed and represented as a graph of occupations and skills.…”

Section: ) Job and Mooc Recommendationmentioning

confidence: 99%

“…Such salience is computed through labeled segments and sentences of the job ad. Similarly, the authors of [133] computed a measure of skill importance for each occupation in each country, using the Revealed Comparative Advantage (RCA) measure, inspired by the work in [138]. This enables to focus on skills that are over-expressed in occupations.…”

Section: ) Skill Saliencementioning

confidence: 99%

A Survey on Skill Identification From Online Job Ads

2021

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“…To accomplish that, we resort to HSS, a measure of pairwise semantic similarity in taxonomies developed in (Giabelli et al 2021), which measures semantic similarity in a taxonomy based on the structure of the hierarchy itself, preserving the semantic similarity intrinsic to the taxonomy.…”

Section: Introductionmentioning

confidence: 99%

JoTA: Aligning Multilingual Job Taxonomies through Word Embeddings (Student Abstract)

Giabelli

Malandri²,

Mercorio

et al. 2022

AAAI

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We propose JoTA (Job Taxonomy Alignment), a domain-independent, knowledge-poor method for automatic taxonomy alignment of lexical taxonomies via word embeddings. JoTA associates all the leaf terms of the origin taxonomy to one or many concepts in the destination one, employing a scoring function, which merges the score of a hierarchical method and the score of a classification task. JoTA is developed in the context of an EU Grant aiming at bridging the national taxonomies of EU countries towards the European Skills, Competences, Qualifications and Occupations taxonomy (ESCO) through AI. The method reaches a 0.8 accuracy on recommending top-5 occupations and a wMRR of 0.72.

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Skills2Job: A recommender system that encodes job offer embeddings on graph databases

Cited by 48 publications

References 23 publications

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

A Survey on Skill Identification From Online Job Ads

JoTA: Aligning Multilingual Job Taxonomies through Word Embeddings (Student Abstract)

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