A Robust and Fair Multimodal Recommender System Under Structured Modality Missingness: The Trust-Based Evaluation Framework

Abstract

The growing complexity of digital real estate platforms demands intelligent recommendation systems (RS) capable of operating in data-sparse and heterogeneous environments. While transfer learning (TL) has proven effective in general RS, its application to real estate (RE) remains limited, particularly regarding the operationalization of multi-dimensional evaluation frameworks. This study addresses these gaps by developing a TL-based real estate recommender system (RERS) utilizing a pre-trained ResNet50 architecture, trained on a locally curated dataset from Gauteng, South Africa, providing rare, data-driven insights into a pivotal emerging market economy. By transitioning from traditional label-based retrieval to high-dimensional visual feature alignment, the model mitigates class imbalance and data redundancy in fragmented property markets. The framework is validated using the proposed Trust-based Evaluation (T-EVAL) methodology, demonstrating the efficacy of deep learning architectures in providing reliable and trustworthy property recommendations within emerging market economies.