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🦀 ClawHub

Restaurant Crosscheck CN

by @zhongrenfei1-hub

Cross-reference restaurant recommendations from Xiaohongshu (小红书) and Dianping (大众点评) to validate restaurant quality and consistency. Use when querying resta...

Versionv2.0.0
Downloads1,169
Installs4
#database#social-media#legal#cooking
TERMINAL
clawhub install restaurant-crosscheck-cn

📖 About This Skill

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

    • 💡 Examples

    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

    ⚙️ 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"]
}

    📋 Tips & Best Practices

    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