Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 4: Specialization vs Generalization

In Part 4 of a series on fine-tuning sparse embeddings for e-commerce search, the focus is on the trade-off between specialization and generalization of a SPLADE model trained on Amazon ESCI data. The model significantly outperforms BM25 in in-domain tests, but its performance varies in cross-domain evaluations, with improvements observed in other e-commerce datasets like Wayfair and Home Depot due to shared structural elements, while performance drops in general web search (MS MARCO) due to overfitting to e-commerce-specific patterns. To address generalization issues, a multi-domain model trained on combined datasets from Amazon, Wayfair, and Home Depot shows balanced improvements across domains, demonstrating the potential of diverse training data in retaining general language understanding while offering better cross-domain transfer. The document also outlines scenarios for choosing between domain-specific fine-tuning and multi-domain training, emphasizing the benefits of fine-tuning for single retailers with extensive data and multi-domain training for platforms serving multiple retailers. The text concludes by suggesting further enhancements, such as cross-encoder reranking and using larger base models, to improve e-commerce search capabilities.