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