Yes, you can use contrastive learning to improve product similarity modeling. Contrastive learning is a machine learning approach that trains models to distinguish between similar and dissimilar data pairs. In the context of product similarity, this means teaching a model to pull representations of related products closer together in a shared embedding space while pushing unrelated products apart. For example, if two T-shirts are often purchased together or share attributes like color and style, their embeddings would align. Conversely, a T-shirt and a coffee mug would have distant embeddings. This method works well because it directly optimizes for similarity relationships rather than relying on indirect metrics like keyword overlap.
A practical implementation might involve training a neural network using a contrastive loss function, such as triplet loss or NT-Xent loss. For instance, in an e-commerce setting, you could create triplets of products: an anchor (e.g., a red sneaker), a positive sample (another red sneaker from a different brand), and a negative sample (a blue sandal). The model learns to minimize the distance between the anchor and positive samples while maximizing the distance to the negative sample. This approach can handle complex relationships, such as matching products with varying descriptions (e.g., “mobile phone” vs. “smartphone”) or images taken from different angles. Tools like TensorFlow or PyTorch simplify building such models, and frameworks like Sentence-BERT or CLIP can be adapted for text or image-based product data.
However, success depends on data quality and sampling strategies. For example, if your negative samples are too easy (e.g., comparing shoes to books), the model won’t learn fine-grained distinctions. Instead, focus on “hard negatives” like shoes of a similar style but different sizes. Additionally, combining multiple modalities—such as product descriptions, images, and user behavior—can enhance results. A real-world example is Shopify’s product recommendation system, which uses contrastive learning to unify text and image embeddings for better cross-modal retrieval. While contrastive learning requires careful tuning, it’s a robust way to capture nuanced product relationships that traditional methods like cosine similarity on TF-IDF vectors might miss.