Fix discovery scoring: cap trending, prevent score inflation, add freshness
- Cap trending base_score at 18.0 (was unbounded — a viral channel could score 240+ vs search's 15, making everything else invisible) - Cap all discovery scores at 50.0 globally so no single signal dominates - Fix score accumulation: cap accumulated total at 50.0 (was unbounded across repeated runs, cementing high-score channels in top positions forever) - Expire unseen queue entries older than 14 days at start of each run - Add ±8 score perturbation to discovery list endpoint (was pure score DESC, identical every visit until dismissed) - Add score perturbation to discovery_videos ORDER BY too - Fix SQL injection in update_category_clusters (category strings were interpolated directly into query; now use parameterized queries per category) - Raise category signal score from 3.0 → 5.0 to compensate for trending cap Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -1,4 +1,5 @@
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import json
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import random
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from typing import Optional
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from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException
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@@ -81,6 +82,9 @@ def list_discovery(
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pass
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rows = [r for r in rows if neg_hit.get(r["channel_id"], 0) < 3]
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# Add score perturbation so the list doesn't look identical every visit.
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# ±8 jitter keeps relative ranking meaningful while surfacing different channels.
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rows = sorted(rows, key=lambda r: r["score"] + random.uniform(-8, 8), reverse=True)
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rows = rows[:limit]
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items = []
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for row in rows:
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@@ -210,7 +214,7 @@ def discovery_videos(
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)
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)
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WHERE rn <= 2
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ORDER BY score DESC, rn ASC, RANDOM()
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ORDER BY (score + (RANDOM() * 10 - 5)) DESC, rn ASC
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LIMIT :limit OFFSET :offset
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"""),
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{"user_id": current_user.id, "limit": limit, "offset": offset},
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@@ -77,14 +77,18 @@ def _upsert_channel(db: Session, channel_data: dict) -> Channel | None:
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return channel
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_MAX_DISCOVERY_SCORE = 50.0
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def _add_to_discovery(
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db: Session, user_id: int, channel_id: int, score: float, source: str,
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preview_json: str | None = None,
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):
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score = min(score, _MAX_DISCOVERY_SCORE)
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existing = db.query(DiscoveryQueue).filter_by(user_id=user_id, channel_id=channel_id).first()
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if existing:
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# Accumulate scores across sources but cap to prevent one dominant signal
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existing.score = existing.score + score * 0.5
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# Accumulate across sources but cap so no single signal dominates forever
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existing.score = min(existing.score + score * 0.5, _MAX_DISCOVERY_SCORE)
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if preview_json and not existing.preview_json:
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existing.preview_json = preview_json
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return
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@@ -322,22 +326,26 @@ def update_category_clusters(db: Session, user_id: int):
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if not top_categories:
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return
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placeholders = ",".join(f"'{c}'" for c in top_categories)
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candidate_rows = db.execute(
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text(f"""
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SELECT DISTINCT v.channel_id
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FROM videos v
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WHERE v.category IN ({placeholders})
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AND v.channel_id NOT IN (
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SELECT channel_id FROM user_channels WHERE user_id = :user_id
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)
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LIMIT 100
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"""),
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{"user_id": user_id},
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).mappings().all()
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# Use JSON_EACH / parameterized IN via repeated queries to avoid SQL injection
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candidate_channel_ids: set[int] = set()
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for cat in top_categories:
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cat_rows = db.execute(
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text("""
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SELECT DISTINCT v.channel_id
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FROM videos v
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WHERE v.category = :cat
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AND v.channel_id NOT IN (
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SELECT channel_id FROM user_channels WHERE user_id = :user_id
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)
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LIMIT 50
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"""),
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{"cat": cat, "user_id": user_id},
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).mappings().all()
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for row in cat_rows:
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candidate_channel_ids.add(row["channel_id"])
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for row in candidate_rows:
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_add_to_discovery(db, user_id, row["channel_id"], score=3.0, source="category")
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for channel_id in candidate_channel_ids:
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_add_to_discovery(db, user_id, channel_id, score=5.0, source="category")
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db.commit()
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@@ -548,7 +556,9 @@ def update_trending_signal(db: Session, user_id: int, regions: list[str]):
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if uc and uc.status in ("followed", "dismissed"):
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continue
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base_score = float(info["count"]) * 4.0 * len(info["regions"])
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# Cap base_score so a viral trending channel can't dominate the whole queue.
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# count × 4.0 × regions can reach 300+ without this cap.
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base_score = min(float(info["count"]) * 4.0 * len(info["regions"]), 18.0)
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# Tag relevance: positive for liked content, negative for dismissed/disliked.
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# tag_profile comes from user_tag_affinity which tracks both signals.
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@@ -561,7 +571,7 @@ def update_trending_signal(db: Session, user_id: int, regions: list[str]):
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for tags_json in tag_rows:
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tag_boost += _tag_relevance_score(tag_profile, tags_json)
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final_score = base_score + tag_boost
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final_score = min(base_score + tag_boost, 25.0)
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if final_score <= 0:
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continue
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@@ -646,6 +656,19 @@ def update_graph_signal(db: Session, user_id: int):
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def run_full_discovery(db: Session, user_id: int, regions: list[str] | None = None):
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if regions is None:
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regions = ["US", "SE"]
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# Expire unseen entries older than 14 days so stale high-score channels
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# don't block fresh results forever.
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db.execute(
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text("""
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DELETE FROM discovery_queue
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WHERE user_id = :user_id AND seen = 0
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AND created_at <= datetime('now', '-14 days')
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"""),
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{"user_id": user_id},
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)
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db.commit()
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crawl_by_search(db, user_id)
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update_community_signal(db, user_id)
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update_category_clusters(db, user_id)
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