To choose embedding models for e-commerce product search, start by evaluating the type of data you need to process and the trade-offs between accuracy, speed, and scalability. Embeddings convert text, images, or other data into numerical vectors, allowing you to measure similarity between products. For text-based search (e.g., product titles or descriptions), models like BERT, Sentence-BERT (SBERT), or Universal Sentence Encoder (USE) are strong candidates. These models capture semantic relationships, which is critical for handling synonyms (e.g., “cellphone” vs. “mobile phone”) or varied product attributes. For image-based search, models like CLIP or ResNet generate embeddings that link visual features to textual queries. Prioritize models pre-trained on e-commerce-specific data if available, as generic models may not grasp domain-specific terms (e.g., “RGB keyboard” vs. “mechanical keyboard”).
Next, consider whether to use off-the-shelf models or fine-tune them. Pre-trained models work well for general use cases but may struggle with niche product categories. For example, a generic text embedding model might not distinguish between “joggers” (pants) and “joggers” (athletes) without fine-tuning. If your catalog includes specialized items (e.g., industrial parts or luxury fashion), fine-tune a base model like SBERT using your product data. This involves training on pairs of search queries and relevant products to align the embedding space with user intent. Tools like Hugging Face Transformers or TensorFlow make this accessible. However, fine-tuning requires labeled data and computational resources, so weigh the cost against potential gains in search accuracy.
Finally, test the model’s performance in your infrastructure. Smaller models like DistilBERT or MiniLM offer faster inference and lower memory usage, which is crucial for real-time search in large catalogs. Compare embeddings using metrics like recall@k (how often the correct product appears in top results) or latency under load. For example, a model producing 768-dimensional vectors might be more accurate than one with 384 dimensions but could slow down nearest-neighbor search. Use approximate nearest neighbor libraries (e.g., FAISS, Annoy) to optimize retrieval speed. If your product data includes multiple languages, opt for multilingual models like LaBSE or XLM-R. Start with a simple baseline (e.g., TF-IDF or Word2Vec) and iterate—sometimes a lightweight approach suffices for narrow use cases.