- name
- screen-recommendation-loop
- description
- Build and run a low-friction movie/anime recommendation + follow-up loop. Use when a user wants long-term taste profiling from watched/unfinished/dropped feedback, mixed sources (e.g., Douban/Bangumi Top lists), random title-type selection, and automatic type-based follow-up timing.
Screen Recommendation Loop
Overview
Run an ongoing recommendation system that balances consistency and low user burden. Recommend one title at a time, collect short feedback, and adapt future picks from preference signals.
Core Workflow
- Pick one candidate title.
- Send one concise recommendation message.
- Schedule follow-up based on title type.
- Collect status in a small fixed schema.
- Update preference weights.
- Pick the next title with constrained randomness.
Keep each interaction short. Prioritize adherence over perfect metadata.
If the user proactively returns before scheduled follow-up (e.g., "I watched it, let's discuss"), skip waiting and immediately:
- run the review step,
- record status,
- start the next recommendation cycle.
Recommendation Rules
- Use a mixed candidate pool (example: Douban Top 250 + Bangumi Top 250).
- Select title type randomly (not strict alternation):
- allow movie → movie - allow anime → anime
- Apply hard filters before scoring:
- already completed recently - explicitly rejected/dropped for same strong pattern - duplicate title aliases
- Use constrained random ranking:
- exploit known preferences (higher weight) - retain exploration quota (e.g., 15–25%) to avoid tunnel vision
Follow-Up Timing Rules
Use automatic, content-type-based follow-up windows.
Default logic:
- Movie recommendation: follow up at
recommendedAt + 7 days - Anime/series recommendation: follow up at
recommendedAt + 30 days
No manual per-user interval configuration is required; infer from recommended content type.
When asking, send at a random time inside a normal activity window (for example 10:00–22:30 in the target timezone).
Accepted User Statuses
Treat all as valid outcomes:
- watched (completed)
- partial (started but unfinished)
- not_started
- dropped_midway
- reject_this_title
Do not frame partial/dropped as failure. Use them as preference signals.
Feedback Prompt Template
Use a tiny response format:
- status: watched / partial / not_started / dropped_midway / reject_this_title
- one-line feeling (optional)
- next mood (optional): brainy / healing / realistic / light
Preference Update Heuristics
- watched: reinforce nearby tags and narrative patterns
- partial: slight penalty to pacing/length mismatch factors
- dropped_midway: strong negative weight to dominant disliked traits
- reject_this_title: title-level or trope-level block depending on reason
- not_started: no strong taste penalty; treat as scheduling signal
Decay old signals slowly to avoid overfitting to one week.
Minimal Record Schema
Keep per-title state:
- id
- title
- type (movie|anime)
- source
- recommendedAt
- followupAt
- status
- tags (optional)
- note (optional, one-line user feedback)
This can live in JSON or SQLite.
Safety and Privacy
- Never store private identifiers in the skill package.
- Keep the skill generic: no personal names, account IDs, chat IDs, tokens, local paths, or private schedules.
- If publishing, scrub sample data and examples before packaging.