Tencent SkillHub · Data Analysis

餐厅推荐交叉验证

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.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: IMPLEMENTATION.md, INTEGRATION_COMPLETE.md, PUBLISH_GUIDE.md, QUICKSTART.md, QUICK_PUBLISH.md, README.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: liyang2016
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 17 sections Open source page

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

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)

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

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

Category context

Data access, storage, extraction, analysis, reporting, and insight generation.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

6 Docs

IMPLEMENTATION.md Docs
INTEGRATION_COMPLETE.md Docs
PUBLISH_GUIDE.md Docs
QUICK_PUBLISH.md Docs
QUICKSTART.md Docs
README.md Docs