---

name: restaurant-review-crosscheck description: Cross-reference restaurant recommendations from Xiaohongshu (小红书) and Dianping (大众点评) to validate restaurant quality and consistency. Use when querying restaurant recommendations by geographic location (city/district) to get validated insights from both platforms. Automatically fetches ratings, review counts, and analyzes consistency across platforms to provide trustworthy recommendations with confidence scores.

Restaurant Review Cross-Check

Cross-reference restaurant data from Xiaohongshu and Dianping to provide validated recommendations.

Quick Start

Query restaurants by location and cuisine type:

# Basic query
crosscheck-restaurants "上海静安区" "日式料理"

# With filters
crosscheck-restaurants "北京朝阳区" "火锅" --min-rating 4.5 --min-reviews 100

Workflow

1. Data Collection

Query both platforms simultaneously:

Dianping:

• Fetch restaurants matching location + cuisine
• Extract: name, rating, review_count, price_range, address, tags

Xiaohongshu:

• Search notes/posts matching location + cuisine
• Extract: restaurant_name, engagement_metrics (likes/saves), sentiment_score
• Note: Xiaohongshu data requires scraping as no public API

2. Data Matching

Match restaurants across platforms using fuzzy matching:

• Restaurant name similarity (Levenshtein distance)
• Location proximity (address matching)
• Handle name variations (e.g., "银座寿司" vs "银座寿司静安店")

See scripts/match_restaurants.py for matching logic.

3. Consistency Analysis

Calculate consistency score based on:

• Rating correlation (0-1): Correlation between platform ratings
• Engagement validation (0-1): Do high ratings correlate with high engagement?
• Sentiment alignment (0-1): Do user sentiments align across platforms?

Formula: consistency_score = (rating_corr * 0.5) + (engagement_val * 0.3) + (sentiment_align * 0.2)

4. Recommendation Score

Calculate final recommendation score:

recommendation_score = (
    (dianping_rating * 0.4) +
    (xhs_engagement_normalized * 0.3) +
    (consistency_score * 0.3)
) * 10

Output: 0-10 scale, where >8.0 = high confidence recommendation

Output Format

📍 [Location] [Cuisine Type] 餐厅推荐

1. [Restaurant Name]
   🏆 推荐指数: X.X/10
   ⭐ 大众点评: X.X (Xk评价)
   💬 小红书: X.X⭐ (X笔记)
   📍 地址: [Address]
   💰 人均: ¥[Price]
   ✅ 一致性: [高/中/低] - [Brief explanation]
   
   📊 平台对比:
   - 大众点评标签: [Tags]
   - 小红书热词: [Keywords]
   
   ⚠️ 注意: [Any discrepancies or warnings]

[Continue for top 5-10 restaurants...]

Thresholds

• Min rating: 4.0/5.0 (configurable)
• Min reviews: 50 on Dianping, 20 notes on Xiaohongshu (configurable)
• Max results: Top 10 restaurants by recommendation score
• High consistency: Score > 0.7
• Medium consistency: Score 0.5-0.7
• Low consistency: Score < 0.5 (flag for manual review)

API & Data Sources

Dianping

• Method: Web scraping (Dianping API requires business partnership)
• Base URL: https://www.dianping.com
• Rate limiting: 1 request/2 seconds minimum
• Anti-scraping: Use residential proxies, rotate user agents

See scripts/fetch_dianping.py for implementation.

Xiaohongshu

• Method: Web scraping (no public API)
• Base URL: https://www.xiaohongshu.com
• Rate limiting: 1 request/3 seconds minimum
• Authentication: Cookies required for full access

See scripts/fetch_xiaohongshu.py for implementation.

Configuration

Edit scripts/config.py to set:

DEFAULT_THRESHOLDS = {
    "min_rating": 4.0,
    "min_dianping_reviews": 50,
    "min_xhs_notes": 20,
    "max_results": 10
}

PROXY_CONFIG = {
    "use_proxy": True,
    "proxy_list": ["http://proxy1:port", "http://proxy2:port"]
}

Error Handling

• No matches found: Suggest broader search terms or nearby areas
• Platform timeout: Retry with exponential backoff, max 3 attempts
• Rate limiting detected: Pause for 60 seconds, rotate proxy
• Low confidence results: Flag results with consistency < 0.5 for manual review

Advanced Features

Sentiment Analysis

Xiaohongshu posts use NLP to extract:

• Food quality mentions
• Service quality mentions
• Atmosphere mentions
• Price/value mentions

See references/sentiment_analysis.md for methodology.

Fuzzy Matching

Handle restaurant name variations:

• Chain stores (e.g., "海底捞火锅" vs "海底捞静安店")
• Abbreviations (e.g., "鼎泰丰" vs "鼎泰丰上海店")
• Translation differences

Uses thefuzz library for similarity scoring.

Dependencies

pip install requests beautifulsoup4 pandas numpy thefuzz selenium lxml

See scripts/requirements.txt for complete list.

Troubleshooting

Issue: Xiaohongshu returns empty results

• Solution: Check if cookies expired, re-authenticate

Issue: Dianping blocks requests

• Solution: Reduce request rate, rotate proxies

Issue: Poor matching between platforms

• Solution: Adjust similarity threshold in match_restaurants.py

References

• Data schema documentation
• Sentiment analysis guide
• API limitations