Unlike Kinopoisk or online streaming services, we don’t force you to choose from «everything»; we only show you what you are highly likely to enjoy.Goal: To solve the «choice paralysis» problem and turn film discovery into a fast, engaging ritual.For the user: To reduce the time spent choosing a film through intuitive swiping.For the product: To create a user’s «cinematic DNA» and ensure high conversion to viewing (watchlist), while avoiding interface overload with text.

Segment: Entertainment / Film and Streaming Niche Topic: Gamified movie selection for the evening (Dating app mechanics + AI recommendations).Focus on Emotional Choice: Swiping based on a trailer or atmosphere, rather than ratings or text descriptions.

Direct competitors (swipe apps): Flicksee, AnyFilm, Cineswipe.

Weaknesses: They swipe through posters/descriptions (not videos), group sessions require every participant to install the app, and the algorithms operate as a «black box.»

Indirect competitors (catalogs): Kinopoisk, Letterboxd, IMDb.

Weaknesses: Cluttered interfaces, require deliberate reading and manual filtering, and involve a lengthy selection process.

Conclusion: The swipe app market exists, but these applications fail to solve the problem of collective choice and do not offer transparent reasoning behind their recommendations.

«Swipe through the trailer, not the poster»: assessing the atmosphere and visual style using short video clips, which reduces the risk of getting the «wrong vibe.» «Group matching without installing an app»: friends connect to choose a film via a link (browser / Telegram) and swipe without registering.«Transparent AI and visible filters»: you control the era and genre, and the algorithm honestly explains every choice («We’re showing this because you liked 3 90s films»).

Primary Sources

1. Professional Media: Kinopoisk (news section), Kinometro (Exhibitor’s Bulletin), Variety, RBC Life. (Industry news, release dates, box office analytics, trends)

2. Platforms and Databases: Kinopoisk, IMDb, Letterboxd, TMDB. (Movie metadata, posters, trailers, user ratings, and reviews for taste analysis)

3. Tool Developers / APIs: TMDB API, Kinopoisk API, Amplitude, Mixpanel, Yandex Wordstat. (Documentation for catalog integration, algorithm updates, user behavior analysis, and search demand)

4. Communities: Reddit (r/movies, r/NetflixBestOf), thematic Telegram channels («Cinema, ” „What to watch“), VK public pages, film enthusiast Discord servers. (Discussions, queries like „What to watch tonight, ” user experience, new swipe techniques, and reactions to new releases)

Primary: Mobile Application (iOS / Android) — the core user experience.

Viral (for groups): Web version / Telegram Mini App — allows friends to create a «group match» via a link without installing the application.

Acquisition: TikTok / Reels (visual swipe mechanics, short-form video), cinema-focused Telegram channels, collaborations with public pages.

«Tired Viewer» (18–30 years old).Jobs to Be Done: «When I’m alone in the evening, I want to quickly find a light film that matches my mood so I don’t have to scroll through the catalog for 20 minutes.»«Couple/Group» (25–40 years old).Jobs to Be Done: «When we gather together, we want to select a film that everyone will like to avoid arguments and compromises.»«Film Explorer» (20–35 years old).Jobs to Be Done: «When I’m bored, I want to discover something unusual (a specific genre or era) to get new emotions, rather than watching something popular.»

1. Automated Processing Automated metadata collection from APIs (TMDB, Kinopoisk), extraction of tags from reviews, trailers, and swipe logs, duplicate detection (deduplication), user clustering, and initial recommendation calculation (forming the «cinematic DNA»).2. Human Curation Selection of benchmark films for onboarding («cold start»), verification of metadata accuracy (relevance of streaming availability, trailer quality), quality control of AI explanations and push notifications, and creation of bespoke curated selections.3. Data Structure Every discovered item (film) and every user action (swipe) is converted into a structured record. For a film: ID (TMDB/Kinopoisk), title, year, genres, trailer link, streaming availability, AI tags (mood/atmosphere), and curator status. For a swipe: user_id, card_id, direction (left/right), time card was held on screen, and timestamp.

Collection and Systematization The services use only legal APIs (TMDB, KinoPoisk) and internal application logs. Original movie identifiers (IDs), trailer links, and precise release years are saved. Card duplicates are excluded (deduplication). Parsing private or pirated databases is strictly prohibited.Processing and Categorization AI algorithms generate mood tags, cluster users based on «cinematic DNA,» and produce explanations for recommendations (e.g., «why this movie was shown to you»). Basic metadata (genre, director, actors) remains unaltered, and the AI-generated explanations must be transparent and not perceived by the user as absolute truth.Recommendation and Editorial Control No movie enters a personal feed during a «cold start» without prior human curation. The editor/product owner is responsible for the tone of push notifications, preventing spam, and ensuring the correct functioning of group matches (preventing social pressure on those who swipe left).Publication and Copyright Automated services assist with sending match notifications and generating personalized selections. The use of posters, videos, and text is accompanied by source attribution and adherence to licensing terms (e.g., YouTube API rules for trailers). Communication frequency is strictly regulated and conforms to the application’s tone of voice.