Product Selector

Fusion 5.12
    Fusion 5.12

    Semantic vector search

    Semantic vector search uses machine learning models to determine context and meaning behind search queries and deliver relevant results.

    Semantic vector search (SVS) is the process of interpreting the intent and contextual meaning of queries and content. This is unique from the lexical approach to search, which is driven by keyword matching.

    SVS uses deep metric learning to train a semantic model. The model learns to organize products and queries in a vector space such that similar things are placed near each other and dissimilar things are pushed far apart. At query time, SVS uses the semantic model to embed the query in the vector space and then find nearest neighbor products. This is searching by meaning.

    Semantic Vector Search advantages:

    1. Rich semantic capabilities for better relevancy than lexical search

    2. Fixes problems:

      1. Misspelled queries, synonyms, and phrases

      2. Underperforming and zero-result queries

    3. Doesn’t require signals to start

      1. Uses general pre-trained models for cold-start solution

    4. Collected signals are used to improve relevancy

      1. Models are directly trained to rank items higher when users are more likely to interact with them

    For more information, see How to Set Up SVS