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RECOMMENDER SYSTEMS · RETRIEVAL TO DECISION

Rank choices without hiding uncertainty.

A practical laboratory for candidate generation, personalised scoring, constraint-aware re-ranking and honest evaluation—ending with an agent that can explain or abstain.

Open the ranker
01RetrieveBuild a candidate set
02ScoreEstimate utility
03Re-rankApply constraints
04EvaluateMeasure outcomes
05Explain / abstainRespect the boundary

INTERACTIVE RANKING STUDIO

See every decision in the pipeline.

Each control changes the ranked list, its explanation and at least one evaluation measure. Unsafe or weak candidates never become recommendations.

Exploration

RANKED OUTPUT

Recommended next studies

Hybrid ranking ready.

    Precision@4—Relevant items shown
    Recall@4—Relevant set recovered
    NDCG@4—Graded ranking quality
    Intra-list diversity—Distinct topics surfaced
    Candidate and safety audit

    14-PART CURRICULUM

    From objective to accountable deployment.

    Move in order or inspect a single layer. Every stage separates what the method optimises, what evidence can establish and where judgement remains necessary.

    Core idea

    Inspect

    Failure mode

    Evidence boundary

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    BOUNDED AGENT CONNECTION

    The agent asks. The recommender ranks. Policy decides.

    AgentSupplies context and requests candidates
    RecommenderRetrieves, ranks, explains or abstains
    Policy / humanSets eligibility, safety and action authority

    Non-negotiable boundary: an agent cannot override eligibility filters, lower the safety threshold, invent evidence or convert a ranking score into scientific truth.

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