How deeply does Knowledge Graphs and LLMs in Action cover collaborative filtering?

Defines applied uses and coverage limitations, but stops short of algorithmic implementation details.

Collaborative filtering

As explained in Knowledge Graphs and LLMs in Action

Collaborative filtering is a recommendation technique that infers preferences or associations from patterns across users, items, or entities.

Collaborative filtering uses recommendation-system patterns to infer associations among entities. In pharmaceutical knowledge graphs, it can predict relationships involving drugs, diseases, and molecular targets. section 4.3

Pharmaceutical applications include:

Inferring drug–disease associations for repurposing candidates. section 4.3
Inferring drug–target interactions. section 4.3
Inferring drug–disease treatments from existing graph information. section 4.3

Knowledge-graph drug analysis

Applied to a pharmaceutical knowledge graph, collaborative filtering can rank plausible drug–disease or drug–target links. Its predictions remain limited to drugs and diseases already represented in the graph; combining it with chemical structures, biological processes, and other knowledge may extend coverage to novel compounds. section 4.3

How Knowledge Graphs and LLMs in Action covers it

Cover of Knowledge Graphs and LLMs in Action

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Also explained in

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AI-Powered SearchCollaborative recommendation predicts items or content a user may prefer from the behavior and preferences of similar users.
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The Art of AI Product DevelopmentA collaborative-filtering approach that compares users and recommends items based on the preferences of similar users.
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First-Party Data ActivationA recommendation technique that infers a user's interests from patterns in the preferences or behavior of multiple users.

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Related concepts

recommender systemkind of · A recommender system is software that predicts, selects, or ranks items, content, or actions for users based on available information about users, items, or context.
content-based recommendercontrasts with · A content-based recommender suggests items whose features resemble those of items matching a user’s interests or prior interactions.
behavioral datauses · Behavioral data is information recording the observable actions, interactions, responses, or usage patterns of people interacting with a product, service, or system.
matrix factorizationuses · Matrix factorization is the decomposition or approximation of a matrix as a product of simpler, often lower-dimensional or structured matrices.
data-driven approachkind of · A data-driven approach bases decisions, conclusions, or system behavior substantially on collected or observed data rather than primarily on intuition or assumptions.

This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.