Natural language processing means taking the way humans naturally speak or write and extracting intent from it. In Elevate this is done through combining intelligent query analysis with deep knowledge of various concepts, their significance, and how they relate. Colors, product types, events, materials, and shapes are some of the concepts used.
Concept differentiation
By differentiating between concepts to deduce the visitor's intent, products can be ranked with that in mind. For example, a visitor searching for "light blue" is more likely searching for a garment in a light shade of blue, rather than a blue light, or a lightweight blue garment.
Intelligent prefix search
With regular prefix search, the visitor always gets an expanded view of the search phrase. For example a search for "blue sh" might result in blue shorts, shirts, shirt dresses, and so on. This is great in many cases but can also mean that some irrelevant results are mixed in. With intelligent prefix search, results not matching the perceived visitor intent can be buried or even omitted.
For example, while a visitor is typing "blue shirt" in the search field, phrase and product suggestions may include "blue shirt dresses". The visitor intent is not known as it is not clear if the visitor has finished typing. If a visitor executes a search on "blue shirt" however, Elevate understands that blue shirt dresses are less relevant and will be buried or omitted entirely.
Search result expansion
Sometimes a search result benefits from being expanded with additional products or product types, but all results must still be relevant to the visitor's search intent. Applying data knowledge of concept relations, Elevate can be used to expand search results with closely related products.
For example, a search for "green cardigans" may additionally to green cardigans, display green sweaters at the bottom.
Multi-language search support
For many sites, some visitors use English in their search phrases despite the site being localized in a different language. Searches and completions with known concepts in English are automatically understood and catered, even if product data is provided in the locale of the market. This reduces the need for manually localized synonyms for known concepts.
Automated conceptual synonyms
Since concepts are automatically extracted from the data and a concept can be identified through multiple terms, known synonyms automatically works for extracted concepts without any manual work.
The search engine applies knowledge of the fact that a concept can be expressed in multiple terms during both data processing and query analysis. This means that products are automatically made findable using terminology that does not appear in the product data at all. An example of a known synonym concept is "lady" to "woman". Even if "lady" is not part of a product's data, but "woman" is, Elevate finds the product if "lady" is used in the search.
Hierarchical concept query analysis
Since Elevate understands concept hierarchies, all identified concepts are automatically made findable through known ancestral concepts.
For example, a "light blue shirt" is automatically understood to also be a "blue shirt". Or "blouse" is automatically a "top", where as all "tops" are not "blouses".
Understanding the relation also improves phrase suggestions to facilitate the user journey. An example is by applying the knowledge that a blouse is a top. A visitor typing "wrap blouse" will not suggest "wrap blouse top" as it would be redundant and the addition of "top" insignificant. When typing "wrap top" however, "wrap top blouse" could be relevant as this refines the result.
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