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Inner product

As explained in Knowledge Graphs and LLMs in Action

A dot product is the scalar obtained by multiplying corresponding components of two equal-length vectors and summing the results.

A dot product multiplies corresponding components of equal-length vectors and sums the results. In heterogeneous graph link prediction, it combines user and movie embeddings into a similarity score representing the likelihood of a rating relationship. section 12.2.4

A movie-recommendation link-prediction pipeline uses the operation as its final scoring step:

  • Update both node types with a heterogeneous GNN that aggregates neighboring information. section 12.2.4

How Knowledge Graphs and LLMs in Action covers it

How deep it goes
Defines the scoring role and shows its classifier call, but does not derive the arithmetic or explain optimization internals.
Examples
Uses user–movie ratings, genre features, and a PyTorch DotProduct classifier in an encoder–decoder implementation.
What it assumes
Assumes familiarity with embeddings, node features, graph edges, and heterogeneous GNN encoders.
What you can do after
Apply it via a DotProduct classifier to score candidate links between learned user and movie embeddings.
How it connects
Connects embeddings, heterogeneous graph neural networks, link prediction, similarity scores, and recommendation systems.
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