Smart Jobs

Smart Popular jobs adjust the rating threshold according to a candidate’s popularity. Highly popular titles may clear a lower quality bar, while less-visible titles must earn a stronger rating to pass.

Settings

Setting	Purpose
base_min_rating	Baseline rating around which the adaptive threshold moves
adjustment_factor	How strongly popularity changes that threshold
limit	Maximum candidates discovered before evaluation
rule_set_id	Movie or show rules applied alongside adaptive scoring

Configure Smart Popular from the Jobs UI. The editor stores these values in the same dynamic job model as every other discovery type.

Rules still apply

Smart rating is one decision input, not a replacement for the assigned rule set. Genre, language, keyword, year, runtime, vote, and title-exception decisions still apply.

Avoid duplicating the adaptive threshold with an unnecessarily strict minimum-rating boundary. Use a minimum-votes boundary when you want to reject ratings based on very small samples.

Tune safely

1. Begin with a moderate baseline and adjustment factor.
2. Preview the job several times.
3. Inspect decision details for unexpected candidates.
4. Change one setting at a time.
5. Review the Job Run distribution after enabling it.

A higher baseline is stricter everywhere. A higher adjustment factor makes popular titles more forgiving and less-popular titles stricter.

Troubleshooting

• Too many weak titles: increase base_min_rating, add a minimum-votes boundary, or tighten the assigned rules.
• Weak blockbusters pass: lower adjustment_factor or raise base_min_rating.
• Too few results: inspect whether the assigned rule set is rejecting candidates before changing smart settings.
• Unexpected delivery: open the title’s Activity Entry and inspect the complete decision details.