Bias-Free Hybrid Explainable Recommendation System with Role-Based Dashboard

Abstract

The abstract outlines the challenge of understanding the necessity of the creation of recommender systems in a rapidly changing online marketplace. Recommender systems help customers to make sense of the overwhelming amount of choices available on the internet, however, many traditional recommendation models do not provide an explicit bias indicator and therefore often produce recommendations that favour the more popular products, without any insight as to how those recommendations are generated. A number of empirical studies conducted recently have demonstrated that the association between biased exposure, black-box deep learning algorithms and the way in which items from the long-tail category are treated unequally leadstonegativeuserexperiencesandprohibitslong-tailsellers frombeingabletocompetewithlargere-commerceplatforms. This paper will present an alternative method of creating a recommender system that eliminates the bias associated with the traditional collaborative filtering and content-based methods. Specifically, the Bias-Independent Hybrid Explanation based Recommender System will utilise deep neural networks as an additional source of information to produce higher quality recommendations and minimise the chances of placing products into unfair exposure patterns. In addition, the proposed method will also incorporate SHAP (SHapley Additive exPlanations) and the LIME (Local Interpretable Model-agnostic Explanations) model of explanationsinordertoimprovethetransparencyofthemodel. In addition, this paper includes the introduction of a Fairness-Aware Re-Ranking method and an Exposure Balance Mechanism to deal with the issues associated with fairness.

Introduction

The rapid growth of digital platforms and e-commerce has made recommendation systems essential for helping users discover relevant products while improving customer satisfaction and business revenue. Traditional recommendation approaches, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), face challenges including popularity bias, data sparsity, limited diversity, and poor transparency. Popularity bias disproportionately favors well-known products and large vendors, reducing visibility for small businesses, MSMEs, and regional sellers. Additionally, many deep learning recommendation models function as "black boxes," making it difficult for users to understand why products are recommended.

To overcome these limitations, the proposed research introduces a Hybrid, Fairness-Aware, and Explainable Recommendation System tailored for Indian e-commerce platforms. The system combines Collaborative Filtering, Content-Based Filtering, and lightweight deep learning techniques to improve recommendation accuracy and diversity. Fairness-aware methods, including exposure balancing, fair sampling, and re-ranking algorithms, are incorporated to reduce popularity bias and provide equitable exposure for smaller sellers. Explainable AI (XAI) techniques such as SHAP and LIME are integrated to generate clear, human-understandable explanations for recommendations, enhancing transparency and user trust. The framework also includes role-based governance dashboards for users, managers, and administrators to monitor recommendation quality, fairness metrics, and model performance while complying with Responsible AI principles and India's Digital Personal Data Protection Act (2023).

The proposed architecture follows a three-tier design consisting of a Next.js/React frontend, a Node.js/Express backend, and a FastAPI-based machine learning microservice with MongoDB for data storage. The hybrid recommendation score combines collaborative, content-based, and neural recommendation outputs, while fairness-aware re-ranking adjusts item exposure to improve equity. The system supports personalized recommendations, feedback collection, fairness monitoring, model versioning, and real-time explanations. Evaluation focuses on recommendation accuracy (Precision@K and NDCG), fairness metrics, explainability, and governance capabilities. Experimental results indicate that the hybrid model enhances recommendation relevance, significantly reduces exposure imbalance through fairness-aware ranking, and improves user trust through explainable recommendations, making it a scalable and responsible solution for modern e-commerce environments.

Conclusion

Conclusions This project has created a Bias-Free Hybrid Explainable Recommendation System to address major issues that face today’s e-commerce platforms, including popularity bias, lack of transparency, and limited oversight. The system combines collaborative filtering with content-based filtering techniquesandlightweightdeeplearningtechniquestoprovide higher quality and more diverse sets of product recommendations. In addition, fairness-aware methods of re-ranking and exposure balancing provide for a more equitable distributionofvisibilityfortherecommendeditems,especially for smaller or newer sellers. Users will have increased trust in the recommendations made to them through the incorporation of SHAP and LIME like techniques for explainability since they will offer clear, human-understandable explanations for each recommendation. Role-based dashboards supplement the oversight of the system because managers will be able to see thefairnessmetrics,modelbehaviour,andexposurepatternsin real-time. In summary, the project has taken a thoughtful and responsibleapproachtocreatingtransparent,fair,andeffective recommendation systems.

References

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Copyright

Copyright © 2026 Anjali Kumari, Vishvajeet Kumar, Dr. Mukesh Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.