A Temporal-Weighted Hybrid Recommender for B2B Vehicle Auctions Using Word2Vec Embeddings
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Abstract
Used car auction platforms face unique challenges in personalized recommendation due to extreme data sparsity, high inventory turnover, and real-time operational constraints. This study develops and evaluates a hybrid recommendation system combining Word2Vec embeddings for categorical vehicle attributes with standardized numerical features, applying temporal decay weighting to prioritize recent user interactions. Deployed on Azure infrastructure, the system was evaluated using 12 months of transaction data from a Turkish B2B auction platform comprising 5,322 users, 24,987 vehicles, and 1.87 million interactions. Offline evaluation demonstrates superior performance over baselines (Hit Rate@10: 0.456 vs 0.234 popularity baseline, 94.9% improvement). Production deployment over six months (April–September 2025) generated 977 recommendation-driven sales representing 15.26% of total platform transactions and 17.27M TL in commission revenue. Quasi-experimental analysis revealed a 26.7% increase in monthly purchase frequency among active users, yielding 420 incremental transactions. Results demonstrate how interpretable temporal-weighted embedding models generate measurable commercial value in high-turnover, data-sparse B2B marketplaces.
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