PROPOSED AI APPLICATION

Personal screening recommender

Users cannot find relevant screenings

Cinema and screening operatorsHistorical data model pilotProposed solution scope

The problem and the proposed outcome

Application: Cinema and screening operators — Personal screening recommender
Problem to solve: Users cannot find relevant screenings
Target buyer: Cinema managers, screening clubs
Intended use: Personal / individual own use
Benefit to test: Test whether users find relevant approved options with less search effort.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Semantic recommendation and candidate ranking
Method code: M
AI contribution: Use embeddings, learned ranking or feedback models to shortlist approved options against explicitly stated requirements; show reasons and let users choose.
Output artifact: Evidence-backed shortlist with explanation and user feedback.
Core modules: Approved options / preferences → semantic ranking → reasoned shortlist → user selection
Simple baseline: Catalogue filters, keyword matching and manual shortlisting.
Why test AI: Could improve relevance when requirements are semantic or feedback-rich; simple filters may be sufficient for small catalogues.

Pilot track: Historical data model pilot
Required data: Consented preferences; licensed catalogue; availability
Data readiness: An approved option catalogue, lawful stated preferences or requirements, representative relevance judgements and feedback are needed.
First development step: Define permitted options and relevance criteria, collect reviewed query-to-option examples and compare catalogue filters.
Initial MVP scope: One defined task, one permitted dataset and one reviewer workflow. Manual upload or authorized export first; no unattended external action. Validate the model before live integration.
Demo data: Synthetic or licensed public samples may illustrate the workflow. Real benefit and model performance need representative authorized held-out data.
Buyer validation question: Ask Cinema managers, screening clubs: show a recent case where users cannot find relevant screenings, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: Recommendation acceptance
Model evaluation: Measure precision or ranking relevance at the shortlist size, user-rated usefulness, coverage and acceptance; inspect consequential errors.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: No special flag; domain review still applies
Human review boundary: Reviewer or user confirms labels, exceptions, links or shortlist relevance; no automatic adverse or consequential decision.
Domain review: Confirm data ownership, access, source rights, reviewer authority and correction routes with the operator.
Related micro-ERP catalogue IDs: IWA-B-D39D9D60C711; IWA-B-640635CF0C2D; IWA-B-A8DE080542E8
Integration status: Potential segment link only; no product integration implemented.