Optimizing Transaction Matching Performance Using Hybrid Collaborative Filtering and Deep Learning: An Empirical Analysis of Feature Engineering and Similarity Metrics

Abstract

This paper presents a comprehensive empirical analysis of transaction-matching optimization in commercial real estate markets by integrating collaborative filtering and deep learning techniques. We address critical challenges in buyer-seller matching by developing a hybrid framework that combines matrix factorization-based collaborative filtering with attention-enhanced deep neural networks. Our approach introduces novel feature engineering methodologies designed explicitly for transaction data, incorporating both technical market indicators and behavioral patterns derived from historical transactions. Through extensive experimentation on a dataset of 50,000 commercial real estate transactions, we systematically compare multiple similarity metrics, including cosine similarity, Euclidean distance, and hybrid combinations. The proposed framework achieves 87.3% matching accuracy (Precision@10) and reduces computational latency to 45ms per query, representing significant improvements over baseline methods. Ablation studies reveal that attention mechanisms contribute a 12.4% performance gain, while proper feature engineering accounts for an 18.7% improvement in matching quality.