Related papers: OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System

OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System

URL: http://arxiv.org/abs/2504.07108v1
Date: Mon, 17 Mar 2025 14:12:51 GMT
Title: OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System
Authors: Roan Schellingerhout, Francesco Barile, Nava Tintarev,
Abstract summary: We propose a novel explainable multi-stakeholder job recommender system using graph neural networks.<n>The proposed method is capable of providing both candidate- and company-side recommendations.<n>We find that OKRA performs substantially better than six baselines in terms of nDCG for two datasets.
Score: 2.373992571236766
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The use of recommender systems in the recruitment domain has been labeled as 'high-risk' in recent legislation. As a result, strict requirements regarding explainability and fairness have been put in place to ensure proper treatment of all involved stakeholders. To allow for stakeholder-specific explainability, while also handling highly heterogeneous recruitment data, we propose a novel explainable multi-stakeholder job recommender system using graph neural networks: the Occupational Knowledge-based Recommender using Attention (OKRA). The proposed method is capable of providing both candidate- and company-side recommendations and explanations. We find that OKRA performs substantially better than six baselines in terms of nDCG for two datasets. Furthermore, we find that the tested models show a bias toward candidates and vacancies located in urban areas. Overall, our findings suggest that OKRA provides a balance between accuracy, explainability, and fairness.

Related papers

Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations [2.373992571236766]
We evaluate an explainable job recommender system using a realistic, task-based, mixed-design user study. We find that providing stakeholders with real explanations does not significantly improve decision-making speed and accuracy.
arXiv Detail & Related papers (2024-09-24T11:03:17Z)
Fair Reciprocal Recommendation in Matching Markets [0.8287206589886881]
We investigate reciprocal recommendation in two-sided matching markets between agents divided into two sides. In our model, a match is considered successful only when both individuals express interest in each other. We introduce its fairness criterion, envy-freeness, from the perspective of fair division theory.
arXiv Detail & Related papers (2024-09-01T13:33:41Z)
A Survey on Fairness-aware Recommender Systems [59.23208133653637]
We present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, we delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications.
arXiv Detail & Related papers (2023-06-01T07:08:22Z)
Improving Recommendation Fairness via Data Augmentation [66.4071365614835]
Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes. In this paper, we study how to improve recommendation fairness from the data augmentation perspective.
arXiv Detail & Related papers (2023-02-13T13:11:46Z)
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning [68.25805655688876]
Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. We propose a reinforcement learning based framework, FairRec, to dynamically maintain a long-term balance between accuracy and fairness in IRS. Extensive experiments validate that FairRec can improve fairness, while preserving good recommendation quality.
arXiv Detail & Related papers (2021-06-25T02:02:51Z)
Online certification of preference-based fairness for personalized recommender systems [20.875347023588652]
We assess the fairness of personalized recommender systems in the sense of envy-freeness. We propose an auditing algorithm based on pure exploration and conservative constraints in multi-armed bandits.
arXiv Detail & Related papers (2021-04-29T17:45:27Z)
DeepFair: Deep Learning for Improving Fairness in Recommender Systems [63.732639864601914]
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. We propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users.
arXiv Detail & Related papers (2020-06-09T13:39:38Z)
Fairness-Aware Explainable Recommendation over Knowledge Graphs [73.81994676695346]
We analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users. We propose a fairness constrained approach via re-ranking to mitigate this problem in the context of explainable recommendation over knowledge graphs.
arXiv Detail & Related papers (2020-06-03T05:04:38Z)
A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
arXiv Detail & Related papers (2020-02-28T02:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.