To test different embedding strategies like product-only versus product + reviews, start by defining a clear evaluation framework. First, generate both types of embeddings: product-only (using titles, descriptions, or technical specs) and product + reviews (combining product data with customer reviews). Use the same embedding model (e.g., BERT, Word2Vec, or a custom neural network) for both strategies to isolate the impact of the input data. For example, concatenate product descriptions with review text for the combined approach, ensuring consistent preprocessing (lowercasing, tokenization). Next, apply these embeddings to a downstream task—like product search, recommendation, or classification—and measure performance using task-specific metrics (e.g., accuracy, recall@k, or Mean Reciprocal Rank). This controlled setup lets you compare how each strategy captures semantic relationships.
To evaluate effectively, use both quantitative and qualitative methods. Quantitatively, track metrics aligned with your use case. If testing search relevance, measure how often users click on top results or calculate NDCG (Normalized Discounted Cumulative Gain) to assess ranking quality. For classification, compare F1-scores or precision between the two strategies. Qualitatively, inspect nearest neighbors in the embedding space. For instance, check if a “bluetooth speaker” embedding using reviews clusters products with terms like “long battery life” mentioned in reviews, while the product-only version might miss this. Tools like t-SNE or PCA can visualize clusters for manual inspection. If possible, conduct A/B tests in production: serve recommendations using both strategies to subsets of users and compare engagement metrics like conversion rates or time spent.
Implementation details matter. Suppose you’re building a product search system. For product-only embeddings, you might use a pre-trained sentence transformer on product titles and specs. For the combined approach, append top customer reviews (truncated to avoid exceeding model token limits) to the product text before encoding. Ensure both strategies process the same amount of text (e.g., limit to 512 tokens) to avoid conflating length with strategy effectiveness. If using a custom model, fine-tune it on a task like predicting product categories, then freeze the encoder for embedding generation. Compute embeddings offline for efficiency, and test them on a held-out validation set. For example, an e-commerce platform might find that product + reviews improves search recall by 15% because embeddings capture nuanced features like “waterproof” from reviews that aren’t in product specs. Document computational trade-offs: combining reviews may increase inference latency or storage costs, which could influence the final decision.