So far, your recommendation engine has done a solid job at capturing long-term user preferences. But musical taste isn’t static — what you liked last month might not match your current vibe. That’s where session-aware recommendations come in. In this lesson, you’ll learn how your Smart Music Player now recommends tracks based on real-time listening context — recent moods, tempos, and energy levels — to better reflect what the user is currently into.

You’ll walk through how the backend:

• Scores recent session traits (mood, tempo, energy)
• Filters and re-sorts similarity-based recommendations
• Serves personalized session-aware results through a dedicated API route

You’ll also see how your recommend_tracks_session_aware function intelligently selects music that matches what the user has recently been vibing with.

Located in src/recommend.py, this function upgrades standard similarity-based recommendations by injecting session context.

How It Works (Step-by-Step)

• Step 1: Pull 20 base recommendations from recommend_tracks_by_similarity. This gives tracks the user is likely to enjoy based on embedding similarity.
• Step 2: Call get_recent_session_trends(user_id) to extract session-level stats:
  • common_mood — most frequent mood in recent listens
  • avg_tempo — average tempo
  • avg_energy — average energy
• Step 3: Iterate over the base recommendations and compare each track’s attributes to the trends:
  • +1 if the mood matches
  • +1 if tempo is within 15 BPM
  • +1 if energy is within ±0.2
• Step 4: Sort the tracks by score and return the top N best matches

🧠 The more a track matches the session profile, the higher it ranks.

To make this functionality accessible to the dashboard, you expose it through a dedicated route in app.py:

Key Behavior:

• Accepts a user_id and optional top_n query parameter
• Fetches session-aware recs using the logic you just saw
• Retrieves track metadata and returns it as a structured JSON object

Sample Response:

These tracks were selected not just because the user liked similar music in the past — but because they align with their current listening behavior.

Try It Yourself Head back to the dashboard and click the "Session-Aware" button. Watch how the recommendations change as you simulate new sessions using different moods and energies.

Want energetic recs? Simulate listening to some high-tempo, high-energy tracks. Want chill ones? Log listens for ambient or slow tracks.

You’re no longer just building a recommender — you’re building a context-aware companion.

In this lesson, you learned how to

• You learned how recommend_tracks_session_aware adds real-time session awareness to your recs
• You saw how the backend scores each track based on recent mood, tempo, and energy
• You exposed the logic via a dedicated API route and observed its response structure
• You tested the feature live in the dashboard

Next up: let’s take a closer look at how the session trend analysis works behind the scenes. Stay tuned — the intelligence layer is just getting started.