TV Shows & Streaming Database — Deep Research Brief, Part 07: Tracking, Discovery, Personalization, and Product Features
88 preserved extracts. Research planning document; requested work is not evidence that the work was completed.
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TV Shows & Streaming Database — Deep Research Brief, Part 07: Tracking, Discovery, Personalization, and Product Features
Conduct exhaustive research on the features, data flows, and user experience of TV tracking and discovery products: watchlists, episode progress, scrobbling, imports and exports, calendars, notifications, spoiler protection, recommendations, natural-language and AI search, social features, stats, parental and accessibility filters, and mobile/TV apps. The goal is to produce a benchmarked, prioritized feature specification for the website, with import paths that let displaced users (especially former TV Time users) move in with their history intact, and a clear view of which features competitors do well, badly, or not at all.
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1. How to research this part
Treat every product name, feature, date, and shutdown mentioned here as a lead to verify in the product itself, its help center, changelog, app-store listing, or official blog.
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1. How to research this part
Minimum counts: benchmark 10 competitors against at least 60 features; document import/export formats for at least 8 services; test at least 5 scrobbling integrations; collect at least 300 user complaints/requests from app-store reviews and Reddit tagged by feature.
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1. How to research this part
Prefer hands-on testing with real accounts, official API docs (Trakt API, Simkl API, Plex webhooks, Jellyfin plugins), app-store release notes, and official shutdown/terms notices.
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1. How to research this part
Record app version, platform (iOS/Android/web/TV), country, and test date for every observation; take screenshots.
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1. How to research this part
Do not reverse-engineer private APIs, scrape competitor apps, or automate account actions in violation of terms. Users' own data exports are fine to study with consent; use synthetic or team-owned data for testing.
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1. How to research this part
Label features as observed, documented but not tested, or announced/rumored.
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2. Watchlists, lists, and progress tracking
Watchlist models: single watchlist vs. multiple lists; statuses (plan to watch, watching, paused, dropped, completed, rewatching—MAL/AniList model); custom lists with ordering, notes, collaboration, privacy.
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2. Watchlists, lists, and progress tracking
Episode check-ins: per episode, bulk "mark season watched," "mark watched up to here," watched date (now/air date/custom/unknown), rewatch counts, partial progress (minutes).
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2. Watchlists, lists, and progress tracking
"Up next" logic: next unwatched aired episode; handling specials, skipped episodes, multiple ordering schemes (aired vs. DVD vs. absolute for anime); returning seasons reordering the list.
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2. Watchlists, lists, and progress tracking
Questions: How do Trakt, Simkl, Serializd, TV Time (before shutdown), Hobi, Showly, Sofa, Episode, and IMDb handle each? What do users complain about (accidental bulk marks, missing specials, re-numbered seasons)?
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2. Watchlists, lists, and progress tracking
Produce: state diagram for a user–series relationship and event schema (watched, unwatched, rated, listed, dropped) with idempotency and conflict rules for multi-device sync.
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3.1 Media servers
Plex: webhooks (Plex Pass requirement, verify), Plex's own watch history sync/Discover activity; Trakt's Plex scrobbler (verify status) and community tools (e.g., PlexTraktSync, verify).
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3.1 Media servers
Jellyfin: official/community Trakt plugin, webhook plugin; Emby: webhooks (Premiere, verify), Trakt plugin; Kodi: Trakt add-on (script.trakt), watched-state sync.
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3.1 Media servers
Infuse, Stremio, VLC integrations (verify which support Trakt/Simkl).
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3.1 Media servers
Matching challenges: TVDB vs. TMDB episode ordering in Plex agents, anime absolute numbering, multi-episode files, extras.
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3.2 Platform history exports
Netflix Viewing Activity CSV (Title, Date; profile-level; title string format "Show: Season 1: Episode Title"—verify), plus full "Download your personal information" archive.
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3.2 Platform history exports
Other platforms: Prime Video, Disney+, HBO Max, YouTube (Google Takeout), Apple (privacy.apple.com), Hulu data requests—document what's available, format, delay, and completeness (verify).
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3.2 Platform history exports
GDPR/CCPA data-access requests as a portability route; note where a lawyer should review user-consent and data-handling design.
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3.2 Platform history exports
Browser extensions that capture viewing (e.g., Trakt/Simkl extensions, verify) and terms risks.
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3.2 Platform history exports
Worked example: parse a 2,000-row Netflix CSV; show matching steps (title parse → series candidate → episode title fuzzy match → confidence) and report expected match rate and failure types (localized titles, specials, stand-up, docuseries parts).
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4. Import/export and data portability
Document formats and methods for: Trakt (API, JSON export; VIP/export restrictions changed 2025–2026, verify), Simkl (import tools and export, verify), Serializd (export availability, verify), IMDb (ratings and watchlist CSV export), Letterboxd (CSV export; TV support limited, verify), TV Time (GDPR export and shutdown export tools, verify), MyAnimeList (XML export), AniList (API), MyDramaList (verify), JustWatch (watchlist export, verify), Plex (API).
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4. Import/export and data portability
TV Time shutdown July 15, 2026 (verify): research the announcement, notice period, recommended export paths, which competitors launched importers (Trakt, Simkl, Serializd, others), user sentiment on Reddit and app stores, and how many users were affected (claimed figures, verify). Treat as an acquisition opportunity: what data fields did TV Time exports include (episodes watched, dates, emotions/reactions, comments, followed shows)?
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4. Import/export and data portability
Design an import pipeline: upload → parse → ID resolution (IMDb/TVDB/TMDB IDs where present) → human review queue for low-confidence matches → preview diff → commit → undo.
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4. Import/export and data portability
Export: full user data in JSON + CSV with canonical and external IDs; open format documentation; automated scheduled exports.
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4. Import/export and data portability
Produce: field-mapping matrix (source field → canonical field) for 8+ services and import-success benchmark on sample files.
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5. Calendars and time zones
Release calendar views: my shows, by service, by country, premieres only, finales, returning seasons.
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5. Calendars and time zones
Time-zone logic: U.S. broadcast Eastern/Pacific feeds; streaming drops at 12:00 a.m. PT or 3:00 a.m. ET (Netflix, verify), 6:00 p.m. PT (Disney+ and some Apple shows historically, verify); anime simulcasts JST-based with Crunchyroll release lag; K-dramas KST with Viki/Netflix delays; UK iPlayer box-set releases 6:00 a.m. (verify).
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5. Calendars and time zones
Edge cases: date changes across the International Date Line; DST transitions; episodes that air past midnight counted as previous day; delays for sports preemption.
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5. Calendars and time zones
ICS feed subscriptions (Google/Apple/Outlook) and update behavior.
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5. Calendars and time zones
Produce: release-time rule table (platform × country × typical drop time) with 30+ rows and test cases.
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6. Notifications
Types: new episode available, season premiere dated, renewal/cancellation, now streaming in your country/on your services, leaving soon, price change, friend activity, digest.
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6. Notifications
Channels: push, email, in-app, calendar, RSS.
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6. Notifications
Research frequency tolerance and opt-out patterns; quiet hours; per-show controls; digest vs. real-time.
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6. Notifications
Accuracy risk: premature "available" alerts before regional release; rumored renewals—only confirmed announcements trigger alerts.
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6. Notifications
Produce: notification matrix (trigger, source of truth, confidence required, default on/off, rate limit).
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7. Spoiler shields
Hide episode titles, thumbnails, synopses, cast-death trivia, and ratings for unwatched episodes; blur comments; spoiler tags in reviews; "safe mode" per show.
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7. Spoiler shields
Research existing implementations (Serializd, Trakt, TV Time, Reddit spoiler tags, Letterboxd spoiler flags, Netflix hiding episode descriptions—verify) and failure points (push notifications, search results, cast pages showing "credited in 1 episode" revealing returns).
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7. Spoiler shields
Produce: spoiler-risk inventory of every page element with default treatment.
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8. Recommendations and cold start
Approaches: item-item collaborative filtering, matrix factorization, implicit-feedback models (ALS), sequence models on watch histories, content-based embeddings (genres, tags, synopsis embeddings, cast/crew graph), editorial collections, hybrid re-ranking by availability on the user's services.
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8. Recommendations and cold start
Cold start: onboarding picks (select 10 shows), imports, service selection, popularity by country, mood quizzes.
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8. Recommendations and cold start
Evaluation: offline metrics (recall@k, NDCG, coverage, novelty) vs. online (click-through, adds, completion); fairness toward international and niche titles.
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8. Recommendations and cold start
Research public info on Netflix recommendations (Netflix Prize history; artwork personalization, verify), Trakt, Simkl, Letterboxd-style "similar," TMDB recommendations endpoint.
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8. Recommendations and cold start
Worked example: user imports 40 completed shows; show three candidate recommendation lists (CF, content, hybrid filtered to their services) and explain differences.
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9. Natural-language search and AI assistants
"That show where…" search: character name lookup, plot fragment, actor + year, quote-based (copyright caution), localized titles, typo tolerance.
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9. Natural-language search and AI assistants
Research tools: Reddit r/tipofmytongue patterns; Google/AI Mode results; Netflix AI search tests (verify 2025 OpenAI-powered search beta); Amazon Alexa+ and Prime Video AI features (verify); Google TV Gemini (verify); Apple TV app; startups (verify current list and status).
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9. Natural-language search and AI assistants
Hallucination risks: invented episodes, wrong platform availability, fake renewal claims, made-up cast. Design grounding rules: answers only from database records with citations to record IDs and "last verified" dates; refuse or hedge when not found.
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9. Natural-language search and AI assistants
Produce: evaluation set of 200 queries (100 "that show where," 50 availability, 50 factual) with expected answers and a scoring rubric (correct, partially correct, hallucinated, refused).
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10. Social features, stats, and year-in-review
Social: follow users, activity feed, shared lists, group watch planning, comments per episode, reactions, watch parties (Teleparty and platform-native, verify status of Disney+ GroupWatch removal 2023).
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10. Social features, stats, and year-in-review
Moderation needs for social features (see master brief section 10).
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10. Social features, stats, and year-in-review
Stats: hours watched, episodes, genres, countries, platforms, streaks, most-watched actors; year-in-review share cards (Spotify Wrapped model; Letterboxd Year in Review; Trakt Year in Review, verify).
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10. Social features, stats, and year-in-review
Privacy: public/private defaults, minors, hiding sensitive titles.
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11. Parental guide and accessibility filters
Parental guide: IMDb Parents Guide structure (sex/nudity, violence, profanity, alcohol/drugs, frightening scenes with severity votes), Common Sense Media age ratings (licensing, verify), official ratings (TV-14, BBFC, FSK, ACB), episode-level advisories, content warnings (e.g., suicide depiction warnings added to 13 Reasons Why, verify).
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11. Parental guide and accessibility filters
Accessibility filters: audio description availability by platform/country, SDH captions, dub languages, sign-language versions (e.g., BBC iPlayer signed programs, verify); data sources and reliability.
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11. Parental guide and accessibility filters
Site accessibility: WCAG 2.2 AA, screen-reader-friendly heatmaps, reduced motion, color-blind-safe ratings.
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11. Parental guide and accessibility filters
Produce: filter taxonomy and data-source reliability notes.
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12. Mobile and TV apps
Web-first PWA vs. native iOS/Android; widgets ("up next"), Live Activities, Wear OS/watchOS (verify value); TV apps (tvOS, Android TV/Google TV, Roku, Fire TV, Tizen, webOS) mainly for watchlist handoff and deep-link launching.
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12. Mobile and TV apps
Research platform integrations: Apple TV app "Up Next" partner program restrictions, Google TV watchlist sync, Continue Watching APIs (verify public access).
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12. Mobile and TV apps
App-store fees and subscription rules for premium membership.
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13. UX benchmarking protocol (10 competitors)
Competitors: Trakt, Simkl, Serializd, IMDb, JustWatch, Reelgood, Letterboxd (as UX benchmark), TMDB, Plex Discover, Google TV (alternates: MyAnimeList, AniList, Likewise, Showly, Sofa).
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13. UX benchmarking protocol (10 competitors)
Tasks (timed, scripted, on iOS + web): create account; import history; add 5 shows; mark S1 watched; find where to watch in a second country; set notifications for a premiere; find "shows like X" available on my services; rate an episode; hide spoilers; export data.
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13. UX benchmarking protocol (10 competitors)
Metrics: task success, time on task, taps/clicks, errors, System Usability Scale scores from 8–12 participants per product, accessibility checks (VoiceOver/TalkBack), ad density, paywall friction, performance (Core Web Vitals for web).
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13. UX benchmarking protocol (10 competitors)
Produce: 60+ feature × 10 competitor matrix (has/partial/paywalled/none) and task-level scorecards with screenshots.
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14. Feature prioritization with RICE
Define Reach (monthly users affected), Impact (0.25–3), Confidence (%), Effort (person-weeks).
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14. Feature prioritization with RICE
Worked example: TV Time importer — Reach 20,000 (estimate, verify market sizing), Impact 3, Confidence 50%, Effort 4 → RICE 7,500; compare with episode heatmaps, AI search, TV app.
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14. Feature prioritization with RICE
Combine RICE with dependency (data availability, licensing) and risk (privacy, moderation load) flags.
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14. Feature prioritization with RICE
Produce: prioritized backlog of 50 features with RICE scores and MVP/later cut line.
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15. Timeline 2019 – 2026
Log: Serializd launch (verify year), Trakt VIP and free-tier changes (2025–2026), TV Time ownership (Whip Media) and shutdown (July 15, 2026), Letterboxd acquisition by Tiny (2023), Plex Discover and watch-history features (2022–2024, verify), Disney+ GroupWatch removal, Netflix AI search tests, Google TV Gemini features, Apple TV app changes after Apple TV rename (Oct 2025) — all (verify).
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16. Deliverables for Part 07
Feature specification (60+ features) with acceptance criteria.
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16. Deliverables for Part 07
User–series state model and sync event schema.
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16. Deliverables for Part 07
Scrobbling integration guide (Plex, Jellyfin, Emby, Kodi, Infuse) with matching rules.
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16. Deliverables for Part 07
Import/export field-mapping matrix (8+ services) and Netflix CSV parser spec with match-rate benchmark.
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16. Deliverables for Part 07
TV Time displaced-user research summary and migration plan.
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16. Deliverables for Part 07
Release-time rule table (30+ rows) and time-zone test cases.
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16. Deliverables for Part 07
Notification matrix and spoiler-risk inventory.
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16. Deliverables for Part 07
Recommendation approach comparison and cold-start plan.
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16. Deliverables for Part 07
AI/NL search evaluation set (200 queries) and grounding rules.
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16. Deliverables for Part 07
Parental and accessibility filter taxonomy.
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16. Deliverables for Part 07
UX benchmark report (10 competitors) and complaint dataset (300+ tagged items).
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16. Deliverables for Part 07
RICE-scored backlog (50 features).
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16. Deliverables for Part 07
Glossary of at least 40 terms (scrobble, check-in, absolute order, cold start, NDCG, etc.).
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16. Deliverables for Part 07
Annotated source list of at least 60 sources with access dates.
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Research standards
State the research date; record app versions and test dates for all observations.
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Research standards
Distinguish tested behavior from documentation and from announcements.
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Research standards
Use official APIs and user-consented exports only; no private API reverse-engineering or scraping.
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Research standards
Treat shutdown and user-count claims as claims until confirmed by official notices.
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Research standards
Evaluate AI features for accuracy with a fixed query set; report hallucination rates with sample size.
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Research standards
Flag privacy, minors, and data-portability issues where a lawyer should review.