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Tighten relevance scoring and Polymarket ranking

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Score against original user intent on Reddit, remove the artificial low-end relevance floor, and make Polymarket semantics dominate generic market quality signals.

Also apply the relevance filter to Polymarket and update the affected cross-source tests.

Validation: uv run python -m unittest
This commit is contained in:
Jeffrey Sperling
2026-03-13 19:21:25 -07:00
parent cbee987f65
commit 946af84f9a
12 changed files with 147 additions and 61 deletions
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scripts/lib/polymarket.py
+16 -24
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@@ -13,6 +13,7 @@ from typing import Any, Dict, List, Optional
from urllib.parse import quote_plus, urlencode
from . import http
from .relevance import token_overlap_relevance
GAMMA_SEARCH_URL = "https://gamma-api.polymarket.com/public-search"
@@ -314,8 +315,8 @@ def _shorten_question(question: str) -> str:
def _compute_text_similarity(topic: str, title: str, outcomes: List[str] = None) -> float:
"""Score how well the event title (or outcome names) match the search topic.
Returns 0.0-1.0. Title substring match gets 1.0, outcome match gets 0.85/0.7,
title token overlap gets proportional score.
Returns 0.0-1.0. Exact title phrase match gets 1.0. Otherwise we reuse the
shared query-centric relevance scorer and take the best title/outcome match.
"""
core = _extract_core_subject(topic).lower()
title_lower = title.lower()
@@ -326,27 +327,17 @@ def _compute_text_similarity(topic: str, title: str, outcomes: List[str] = None)
if core in title_lower:
return 1.0
# Check if topic appears in any outcome name (bidirectional)
best_score = token_overlap_relevance(core, title)
if outcomes:
core_tokens = set(core.split())
best_outcome_score = 0.0
for outcome_name in outcomes:
outcome_lower = outcome_name.lower()
# Bidirectional: "arizona" in "arizona basketball" OR "arizona basketball" contains "arizona"
outcome_score = token_overlap_relevance(core, outcome_name)
if core in outcome_lower or outcome_lower in core:
best_outcome_score = max(best_outcome_score, 0.85)
elif core_tokens & set(outcome_lower.split()):
best_outcome_score = max(best_outcome_score, 0.7)
if best_outcome_score > 0:
return best_outcome_score
outcome_score = max(outcome_score, 0.92 if len(outcome_lower.split()) >= 2 else 0.88)
best_score = max(best_score, outcome_score)
# Token overlap fallback against title
topic_tokens = set(core.split())
title_tokens = set(title_lower.split())
if not topic_tokens:
return 0.5
overlap = len(topic_tokens & title_tokens)
return overlap / len(topic_tokens)
return round(best_score, 2)
def _safe_float(val, default=0.0) -> float:
@@ -484,7 +475,8 @@ def parse_polymarket_response(response: Dict[str, Any], topic: str = "") -> List
except (IndexError, TypeError):
end_date = None
# Quality-signal relevance (replaces position-based decay)
# Semantic relevance should dominate. Market quality should refine
# relevant matches, not rescue unrelated high-liquidity events.
text_score = _compute_text_similarity(topic, title, all_outcome_names) if topic else 0.5
# Volume signal: log-scaled monthly volume (most stable signal)
@@ -504,13 +496,13 @@ def parse_polymarket_response(response: Dict[str, Any], topic: str = "") -> List
# Competitive bonus: markets near 50/50 are more interesting
competitive_score = event_competitive
relevance = min(1.0, (
0.30 * text_score +
0.30 * vol_score +
0.15 * liq_score +
market_quality = (
0.50 * vol_score +
0.25 * liq_score +
0.15 * movement_score +
0.10 * competitive_score
))
)
relevance = min(1.0, text_score * (0.75 + 0.25 * market_quality))
# Surface the topic-matching outcome to the front before truncating
if topic and outcome_prices: