Skill: zhongrenfei1-hub/clean-skill (version 1.1.0)

--- name: restaurant-crosscheck-v2 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: ```bash # 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](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](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](scripts/fetch_xiaohongshu.py) for implementation. ## Configuration Edit `scripts/config.py` to set: ```python 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](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 ```bash pip install requests beautifulsoup4 pandas numpy thefuzz selenium lxml ``` See [scripts/requirements.txt](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](references/data_schema.md) - [Sentiment analysis guide](references/sentiment_analysis.md) - [API limitations](references/api_limitations.md)


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