WatchNow AI

Engages users in natural language dialogue to extract viewing preferences, mood states, and genre affinities without requiring structured form submission. The system parses conversational inputs to build a user preference profile incrementally, using dialogue context to disambiguate intent (e.g., distinguishing 'dark' as tone vs. genre). This approach reduces friction compared to traditional rating systems by making preference collection feel like a recommendation conversation rather than a survey.

Generates personalized movie recommendations by identifying users with similar viewing histories and preference patterns, then surfacing titles those similar users rated highly but the target user hasn't seen. The system builds a user-item interaction matrix (ratings, watch history, implicit signals) and applies nearest-neighbor or matrix factorization techniques to find analogous taste profiles. Recommendations are ranked by predicted user rating based on similarity cohorts.

Filters and re-ranks recommendations based on detected or stated user mood (e.g., 'want something uplifting', 'need a dark thriller'). The system maps mood descriptors to movie attributes (tone, pacing, emotional arc) via a mood-to-metadata mapping layer, then applies mood-weighted scoring to adjust recommendation rankings. For example, a comedy might be boosted for 'uplifting' mood but deprioritized for 'intense' mood, even if collaborative filtering ranked it highly.

Continuously updates user preference vectors based on conversational feedback (e.g., 'I didn't like that recommendation because it was too slow'). The system parses feedback to extract preference signals (negative: slow pacing, positive: character-driven), updates the user's preference profile incrementally, and re-ranks future recommendations. This creates a feedback loop where each conversation turn refines the recommendation model without requiring explicit rating submission.

Searches and retrieves movie metadata (title, cast, director, plot, runtime, release year) from an internal or third-party movie database (likely IMDb, TMDB, or similar) to populate recommendations and provide context. The system maps recommended movie IDs to external catalog data, enabling rich recommendation cards with posters, synopses, and cast information. However, the system lacks direct integration with Netflix, Disney+, or Prime Video APIs, so it cannot verify availability or provide direct watch links.

For new users with insufficient rating history, the system falls back to global popularity rankings and genre-based recommendations rather than collaborative filtering. The system identifies the user's stated genre preferences (from chatbot dialogue) and surfaces trending or highly-rated titles in those genres. This provides immediate recommendations while the user builds a rating history, gradually transitioning to personalized collaborative filtering as more preference signals accumulate.

Provides a lightweight chatbot UI in the browser where users can converse with the recommendation engine, ask questions, and receive suggestions. The system manages user sessions (login, session persistence, conversation history) and renders recommendations as chat messages with metadata cards. The interface is stateless per-session but can persist user profiles across sessions if authentication is enabled.

Stores user profiles (ratings, preference vectors, conversation history, mood signals) in a backend database to enable cross-session personalization. The system maintains a preference vector per user (weights for genres, tones, pacing, etc.) that is updated incrementally as the user rates titles or provides feedback. Profiles are retrieved on login, enabling recommendations to be personalized immediately without re-learning preferences.