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.

Configuration

- type: semantic
  encoder:
    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
  encoder:
    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

Example:

p1,1.0,2.0,3.0
p2,2.0,1.5,1.0