By mapping concepts that are similar in meaning to be close in terms of vector distance, semantic vector search is primed for making sense of long-tail queries such as out-of-vocabulary natural language queries, thematic and subjective queries, and also directly addresses the central problems of polysemy and synonymy.
Thematic or subjective queries: Based on rich and detailed catalog data, semantic vector search can directly tackle thematic searches, when customers are vaguely or subjectively referring to a particular class of products, such as “chic dress” or “luxurious watch”. Symptom searches, when customers are referring to a problem rather than a product that solves it, can also be easily supported by semantic vector search based on analysis of customer shopping history.
Polysemy: Polysemy means that the same word may represent different concepts, like the word “table” in “table game” vs “accent table”. When it comes to polysemic queries, semantic vector search easily resolves ambiguity of alternative meanings based on customer behavior.
Synonymy: Synonymy means that the same concept can be represented in many ways by different words, like “Big Apple”, “NYC” and “New York” which all refer to the same city. Semantic vector models support basic synonymy with the help of NLP models which are pre-trained on a large volume of e-commerce data. On top of that, terms used by customers can be associated with relevant products when the semantic search model is trained on customer engagement data.