POST /v1/products/more-like-this endpoint powers distinct recommendation surfaces depending on which embedding columns drive retrieval and ranking. This guide shows five strategies with real requests and live results from indexed catalogs. Parameter reference: More Like This API. A visual version of this guide — the same strategies with product imagery — lives at the interactive demo.
The examples below use two source products:
- Kumi Cropped Jacket — apparel, catalog
lagence_merchant, referenced by UUID (source.uuid). Enriched styles: classic, sophisticated, military-inspired, polished, preppy. Tags: cropped, 3/4 sleeves, gold buttons, embossed buttons, shoulder pads, flap pockets. - Epi Mabillon Backpack Tassil — luxury resale, catalog
fashionphile, referenced by URL (source.url). Enriched styles: luxury, minimalist. Tags: textured leather, silver-tone hardware, adjustable straps, zip closure.
debug: true is included throughout so the response’s effectiveQuery shows the exact query the server executed — remove it in production.
Choosing a strategy
Facet matching is automatic in every strategy: when you don’t pass
include_facets or exclude_facets, the server derives audience facets from the source product’s enrichment. Pass your own facets (for example color_family) on top of any strategy when you want explicit control.
Same look (default)
Omit the embedding-column parameters and the server picks the strategy: query text from the source product’s enriched styles and tags, retrieval over the style and tags embeddings, plus automatic audience facets (product type, gender, age group).Request
effectiveQuery echoes retrievalEmbeddingColumns: ["style_embedding", "tags_embedding"].
Closest attribute matches
Retrieve via the attributes embedding so candidates share concrete product attributes — construction, hardware, closures, silhouette — rather than overall styling.Request
effectiveQuery echoes retrievalEmbeddingColumns: ["attributes_embedding"].
Broad semantic matches
Retrieve via the general-purpose base embedding for wider, discovery-style results.Request
effectiveQuery echoes retrievalEmbeddingColumns: ["embedding"].
Max attribute matches, ordered by look
Two stages working together: retrieve candidates by attribute closeness, then re-score them by style-embedding similarity. This is the combination for related-products rails that should respect concrete attributes but present the closest looks first.Request
effectiveQuery echoes retrievalEmbeddingColumns: ["attributes_embedding"] and rankingEmbeddingColumns: ["style_embedding"].
Same look, lower price
The default strategy constrained to products priced below the source."higher" works the same way in the other direction.
Request
effectiveQuery echoes retrievalEmbeddingColumns: ["style_embedding", "tags_embedding"].
Combining with facets
Any strategy accepts explicit facets. For example, attribute-focused retrieval pinned to the source’s color family:Request