Customizing Smart Ranking

nochannel search offers several ways to fine tune your smart ranking to best suit your store's needs let's explore these options with practical examples search requests (recommended) enable smart ranking for specific searches this allows you to apply smart ranking selectively or differently for certain types of searches example search request { "search" { "query" { "query string" { "query" "designer handbags" } }, "ext" { "search ranker" true }, "tracking" "all" } } this configuration ensures that searches for "designer handbags" use smart ranking, helping to surface the most popular and relevant luxury bags index settings apply smart ranking settings to an entire index this is useful for setting a baseline smart ranking behavior for all your products example configuration { "data" { "index" { "plugin" { "search ranker" { "transformer" { "rescoring" { "enabled" true, "properties" { "tenant id" "fashion emporium", "item id field" "sku", "weight" 0 8 } } } } } } } } in this example tenant id "fashion emporium"` identifies your store item id field "sku"` specifies that you use skus to identify products weight 0 8` sets how strongly smart ranking influences the final order custom search pipelines create advanced smart ranking logic for complex scenarios this is particularly useful for stores with diverse product lines or specialized search needs example custom pipeline { "data" { "description" "seasonal apparel smart ranking", "response processors" \[ { "rescoring" { "tenant id" "fashion emporium", "item id field" "sku", "weight" 0 6, "context" { "season" "summer", "trend factor" 0 3 } } } ] } } this pipeline applies a seasonal context ("summer") incorporates a trend factor to boost trendy items uses a moderate smart ranking weight of 0 6 you might use this pipeline during summer months to ensure that searches for general terms like "tops" or "shoes" prioritize summer appropriate and trendy items the key to effective smart ranking is balancing relevance, business goals, and user experience regularly review and adjust your settings based on performance data and customer feedback