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Raise artist threshold; add title similarity floor

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In web_server.py, increase the minimum artist similarity from 0.4 to 0.5 in both cache validation and candidate scoring. Add a title similarity floor (min_title_similarity = 0.5) to _discovery_score_candidates: compute cleaned/core titles, allow a core-title exact match to bypass the floor, otherwise require title similarity >= 0.5. Only candidates that pass both artist and title floors proceed to full weighted scoring. This reduces false positives where a strong artist match could previously mask a poor title match.
This commit is contained in:
Broque Thomas 2026-02-26 07:54:21 -08:00
parent e77dbf3f1e
commit 3893d75ad0
1 changed files with 27 additions and 10 deletions
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37
web_server.py
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@ -18078,7 +18078,7 @@ def _get_discovery_cache_key(title, artist):
def _validate_discovery_cache_artist(source_artist, cached_match): def _validate_discovery_cache_artist(source_artist, cached_match):
"""Check if a cached discovery match has a valid artist. Returns False if the """Check if a cached discovery match has a valid artist. Returns False if the
cached result's artist doesn't match the source artist (stale/wrong cache entry).""" cached result's artist doesn't match the source artist (stale/wrong cache entry)."""
min_artist_similarity = 0.4 min_artist_similarity = 0.5
source_artist_cleaned = matching_engine.clean_artist(source_artist) source_artist_cleaned = matching_engine.clean_artist(source_artist)
if not source_artist_cleaned: if not source_artist_cleaned:
return True # No source artist to validate against return True # No source artist to validate against
@ -18108,11 +18108,10 @@ def _validate_discovery_cache_artist(source_artist, cached_match):
def _discovery_score_candidates(source_title, source_artist, source_duration_ms, search_results): def _discovery_score_candidates(source_title, source_artist, source_duration_ms, search_results):
"""Score search results against a source track using the matching engine. """Score search results against a source track using the matching engine.
Uses matching_engine.score_track_match() which applies clean_title/clean_artist, Both artist AND title must independently pass minimum similarity floors.
core title fast path, duration similarity, and 60/30/10 weighted scoring. This prevents weighted scoring from allowing a perfect artist to carry a
garbage title (or vice versa). If either dimension doesn't match, the
Includes a minimum artist similarity check to prevent "right title, wrong artist" candidate is rejected — no match is better than a wrong match.
matches (e.g., "Yoga" by bbno$ matching "Yoga" by Sleep Music Lullabies).
Args: Args:
source_title: The source track title (already cleaned for YouTube, raw for others) source_title: The source track title (already cleaned for YouTube, raw for others)
@ -18126,9 +18125,12 @@ def _discovery_score_candidates(source_title, source_artist, source_duration_ms,
best_match = None best_match = None
best_confidence = 0.0 best_confidence = 0.0
best_index = -1 best_index = -1
min_artist_similarity = 0.4 # Reject candidates where artist doesn't match at all min_artist_similarity = 0.5
min_title_similarity = 0.5
source_artist_cleaned = matching_engine.clean_artist(source_artist) source_artist_cleaned = matching_engine.clean_artist(source_artist)
source_title_cleaned = matching_engine.clean_title(source_title)
source_core_title = matching_engine.get_core_string(source_title)
for idx, result in enumerate(search_results): for idx, result in enumerate(search_results):
try: try:
@ -18136,14 +18138,12 @@ def _discovery_score_candidates(source_title, source_artist, source_duration_ms,
result_name = result.name if hasattr(result, 'name') else '' result_name = result.name if hasattr(result, 'name') else ''
result_duration = result.duration_ms if hasattr(result, 'duration_ms') else 0 result_duration = result.duration_ms if hasattr(result, 'duration_ms') else 0
# Quick artist sanity check — reject if artist doesn't match at all # Artist floor — both must match, not just the weighted score
# This prevents "right title, wrong artist" false positives
best_artist_sim = 0.0 best_artist_sim = 0.0
for cand_artist in result_artists: for cand_artist in result_artists:
if not cand_artist: if not cand_artist:
continue continue
cand_cleaned = matching_engine.clean_artist(cand_artist) cand_cleaned = matching_engine.clean_artist(cand_artist)
# Check containment (e.g., "drake" in "drake 21 savage")
cand_normalized = matching_engine.normalize_string(cand_artist) cand_normalized = matching_engine.normalize_string(cand_artist)
if source_artist_cleaned and source_artist_cleaned in cand_normalized: if source_artist_cleaned and source_artist_cleaned in cand_normalized:
best_artist_sim = 1.0 best_artist_sim = 1.0
@ -18155,6 +18155,23 @@ def _discovery_score_candidates(source_title, source_artist, source_duration_ms,
if best_artist_sim < min_artist_similarity: if best_artist_sim < min_artist_similarity:
continue continue
# Title floor — both must match, not just the weighted score
cand_title_cleaned = matching_engine.clean_title(result_name)
cand_core_title = matching_engine.get_core_string(result_name)
# Core title exact match bypasses the floor (e.g., "edamame" == "edamame")
title_passes = False
if source_core_title and cand_core_title and source_core_title == cand_core_title:
title_passes = True
else:
title_sim = matching_engine.similarity_score(source_title_cleaned, cand_title_cleaned)
if title_sim >= min_title_similarity:
title_passes = True
if not title_passes:
continue
# Both floors passed — now do full scoring
confidence, match_type = matching_engine.score_track_match( confidence, match_type = matching_engine.score_track_match(
source_title=source_title, source_title=source_title,
source_artists=[source_artist], source_artists=[source_artist],