Semantic similarity

semantic is a content recommendation model, which computes item similarity only based on a difference between neural embeddings of items.

This model is useful for solving a cold-start problem of recommendations, as it requires no user feedback.


- type: semantic
    type: bert
    model: metarank/all-MiniLM-L6-v2
    dim: 384 # embedding size
  itemFields: [title, description]
  • itemFields: fields which should be used for embedding

  • encoder: a method of computing embeddings

Metarank has quite limited support for embeddings:

  • bert type of embeddings only supports ONNX-encoded models from sentence-transformers from HuggingFace

  • csv type of embeddings allows loading a custom pre-made dictionary.

- type: semantic
    type: csv
    dim: 384 # embedding size
    path: /opt/dic.csv
  itemFields: [title, description]

A dictionary should be a comma-separated CSV-formatted file, where:

  • 1st column is product id

  • 2 till N+1 columns - float values for N-dimentional embedding



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