Update recommender.py

recommender.py

@@ -2,11 +2,11 @@ from skill_extraction import get_skill_embedding
 import numpy as np
 
 def generate_recommendations(db, user_id):
-    # Step 1: Get missing/target recommended skills
-    rec_skills = db.get_recommendations(user_id)  # returns list of skill names
+    # Step 1: Get recommended skills
+    rec_skills = db.get_recommendations(user_id)  # list of skill names
 
-    # Step 2: Get user's existing confirmed skills + embeddings
-    user_skill_data = db.get_user_skills(user_id)
+    # Step 2: Get existing user skills
+    user_skill_data = db.get_user_skills(user_id)  # returns [(skill, conf), ...]
     user_skill_embeddings = [
         get_skill_embedding(skill)
         for skill, _ in user_skill_data
@@ -18,19 +18,20 @@ def generate_recommendations(db, user_id):
     for rec_skill in rec_skills:
         rec_emb = get_skill_embedding(rec_skill)
         if rec_emb is None:
-            results.append((rec_skill, ""))  # fallback without confidence
+            results.append((rec_skill, ""))  # fallback
+            db.log_interaction(user_id, "VIEWED_RECOMMENDATION", skill=rec_skill)
             continue
 
-        # Similarity to best user skill
+        # Compute similarity to user's best skill
         sims = [np.dot(rec_emb, emb) for emb in user_skill_embeddings] if user_skill_embeddings else []
         max_sim = float(max(sims)) if sims else 0.40
 
-        # Normalize & Boost for perceived comparability
-        conf_score = min(round(max_sim * 1.2, 2), 0.98)  # scaled + capped
+        # Scale & normalize confidence
+        conf_score = min(round(max_sim * 1.2, 2), 0.98)
 
         results.append((rec_skill, conf_score))
 
-        # Log recommendation interaction
+        # Log
         db.log_interaction(user_id, "VIEWED_RECOMMENDATION", skill=rec_skill)
 
     return sorted(results, key=lambda x: x[1], reverse=True)