AI Music Recommendation System

    Inspiration:
    Personalised music recommendations are the heartbeat of streaming services. Users want suggestions based on listening history, mood, and activity.

    Target Customers:
    Streaming platforms, music-player apps, and serious music enthusiasts.

    Pain Points:
    Traditional algorithms over-fit to past plays and lack diversity; users waste time hunting fresh tracks.

    Solution (Micro-SaaS):
    An AI engine that analyses listening history, inferred mood, and current activity to generate personalised yet diverse playlists.

    MVP Core Features:

    • Behaviour analytics: Collect listening logs, mood tags, activity signals.
    • Personalised recommendations: Model user taste & context.
    • Diversity injector: Ensure novelty to avert listener fatigue.
    • Real-time updates: Adapt lists with explicit feedback.

    Development Effort (Tech Implementation): Medium. Core = ML + data pipelines.

    • Data wrangling: pandas, numpy.
    • Modeling: scikit-learn or lightfm for collaborative + content; exploration of sequence models.
    • Diversity: surprise library or custom re-ranking.

    Traffic Acquisition & Validation (SEO Enhanced):

    • Step 1 – Validation: Landing "Your Music, Your Way. AI-Powered Recommendations"; embed feedback widget.
    • Step 2 – SEO Growth: Head terms: "AI music recommendation", "personalised music playlist"; long-tails such as "AI music algorithm explained".
    • Site architecture: /tools/music-recommendation-engine plus educational blog posts.

    Competitor Analysis: Spotify, Apple Music, Tidal.
    Our chance: lightweight, embeddable recommendation API with transparent privacy and customisable diversity knobs.