AI Content Recommendation System
A recommendation service for ranking products and content using catalogue data, behavioural signals and configurable merchandising rules.
Project overview
A recommendation service for ranking products and content using catalogue data, behavioural signals and configurable merchandising rules. The engagement covered users, decisions, information flows, integrations and the operational constraints of the business before implementation.
The problem
Large catalogues make it difficult for customers to discover relevant products, while business teams need control over availability, campaigns and priority items.
Proposed solution
Combine user behaviour, product metadata and business rules to generate explainable recommendation sets with experiment and performance monitoring.
Challenges
Important delivery considerations
Provide useful results for new users Prevent unavailable products from being promoted Balance relevance with commercial rules
Outcomes
What the work delivered
more relevant catalogue discovery measurable recommendation experiments clearer merchandising controls
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