What question did this study set out to answer?

The central aim is to create a predictive model that delivers tailored, budget-friendly interior design recommendations.

April 10, 2026Open Access

IDBspRS: An Interior Design-Built Service Package Recommendation System Using Artificial Intelligence

Key Points

The central aim is to create a predictive model that delivers tailored, budget-friendly interior design recommendations.
Developed a hybrid recommendation system combining content-based and collaborative filtering.
Utilized techniques like TF-IDF and logistic regression to capture user preferences and budgets.
Collected primary data from small to medium-sized interior design companies.
Created a user-friendly web application to integrate these machine learning services.
Tested several models including Random Forest, XGBoost, and KNN for performance evaluation.
Achieved an accuracy of 83.62% with the logistic regression hybrid model.
Demonstrated improved customer satisfaction and service customization compared to existing platforms.
Showed significant enhancement in personalization and accessibility for homeowners.

Abstract

Digital transformation in the interior design industry has opened new opportunities for innovation; however, many cost-conscious homeowners still face difficulties in selecting and customizing design packages that achieve a balance between overall cost and sustainable quality. Existing interior design platforms lack seamless support and often require homeowners to invest considerable time and effort to tailor services to their needs while staying within budget. To address these challenges, this paper explores the use of machine learning to build a predictive modelling framework that supports personalized and value-driven interior design recommendations. The proposed approach uses a hybrid recommendation system that combines content-based and collaborative filtering. It also incorporates lightweight techniques such as TF–IDF (Term Frequency–Inverse Document Frequency) and logistic regression to more effectively capture user preferences, budget limits, and several interior-design service categories. Primary data was collected from small to medium-sized interior design companies. To demonstrate the proposed approach, a user-friendly web application tool is developed to integrate machine learning-enabled recommendation services. The resulting solution provides access to professional interior design services, enhancing customization and customer satisfaction while reducing the time and effort required from homeowners. To validate and compare the performance of the proposed approach, several machine learning models including Random Forest, XGBoost and KNN (K-Nearest Neighbors) were tested using standard metrics such as accuracy, precision, recall, and ROC-AUC (Receiver Operating Characteristic-Area Under the Curve). The proposed logistic regression hybrid model achieved the strongest overall results, with an accuracy of 83.62%. These findings demonstrate the significant contribution of this work to enhancing personalization and accessibility in the interior design sector via machine learning-enabled recommendation systems. The proposed approach bridges the gap between expert-level services and financial limits, making it a practical choice for cost-conscious homeowners.

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