Install
openclaw skills install @harrylabsj/dianping-restaurant-guideDianping Restaurant Guide
Analyze Dianping reviews to detect fake feedback, extract genuine sentiment, and deliver personalized restaurant and dish recommendations with authenticity s...
Dianping Restaurant Guide (大众点评探店指南)
Make smarter dining decisions by cutting through Dianping's noise. Detect fake reviews, extract genuine sentiment signals, and get personalized restaurant recommendations with dish-level ordering guides.
Core Capabilities
- Review authenticity analysis: Detect suspicious review patterns — timing clusters, user activity profiles, language similarity, and photo metadata
- Sentiment distillation: Filter out probable fake reviews, then aggregate genuine opinions across taste/environment/service/value
- Personalized ranking: Score restaurants against your preferences (cuisine, budget, occasion, distance)
- Dish-level recommendations: Not just which restaurant — which specific dishes to order, and which to avoid
- Exploration route planner: Optimize a multi-restaurant exploration itinerary by geography
- Polarization decoder: When reviews are split 50/50, analyze what each camp values and help you decide
Workflow (8 Steps)
Step 1: Define Dining Context
Input: User provides:
- Location: City + district/landmark (e.g., "上海静安寺", "北京三里屯")
- Cuisine preference: Type (川菜/日料/西餐/火锅...) or "any"
- Budget: Per-person range (e.g., ¥50-100, ¥200+)
- Occasion: Date / family / solo / group dinner / business
- Distance radius: Walking (1km) / short taxi (5km) / willing to travel
Output: Structured dining profile. Logic: Fill missing fields with sensible defaults (budget = ¥50-150, radius = 3km). Ask clarifying questions for ambiguous inputs.
Step 2: Candidate Discovery
Input: Dining profile. Action: Search Dianping for restaurants matching criteria. Filters:
- Rating 3.5–5.0 (include mid-range; some great finds are under-rated)
- Review count ≥50 (sufficient sample)
- Within radius
- Cuisine match
Output: Candidate list (typically 10–30 restaurants). Logic: Deduplicate chain locations unless user prefers them. Track "hidden gems" (high real-review score despite moderate overall rating).
Step 3: Review Harvesting
Input: Candidate restaurant list. Action: Scrape recent reviews (sample ≥30 per restaurant, prioritized by recency). Data extracted per review:
- Rating (overall + sub-scores)
- Text content + length
- Photos (count + metadata)
- Reviewer profile: registration date, review count, follower count, review frequency
- Timestamp
Output: Raw review corpus per restaurant. Logic: Respect Dianping rate limits. Cache for 1 hour.
Step 4: Fake Review Detection (The Differentiator)
Input: Review corpus per restaurant. Detection signals (multi-dimensional scoring):
| Signal | Red Flag Pattern | Weight |
|---|---|---|
| Time cluster | ≥5 reviews within 30 min | High |
| Reviewer age | Account <7 days old | High |
| Review frequency | >10 reviews/day across restaurants | Medium |
| Text similarity | High cosine similarity with other reviews | High |
| Photo EXIF | Identical camera/settings across "different" users | Medium |
| Generic language | "环境不错""味道很好" without specifics | Low |
| Rating pattern | All 5-star, no detail | Low |
| Reviewer history | Only 5-star reviews, never critical | Medium |
Output: Each review tagged: genuine | suspicious | likely_fake. Restaurant scored with authenticity index (0–100).
Logic: Ensemble scoring with weighted signals. Flag entire restaurant as "suspicious" if >40% reviews are likely_fake. This is the key differentiator from any existing tool.
Step 5: Sentiment Extraction (Genuine Reviews Only)
Input: Filtered genuine reviews. Action: LLM-based sentiment extraction across 4 dimensions:
- Taste (口味): Dish-specific praise/criticism, freshness, authenticity
- Environment (环境): Noise level, seating comfort, decor, cleanliness
- Service (服务): Wait time, staff attitude, ordering convenience
- Value (性价比): Portion size vs price, "worth it" sentiment
Output: Structured sentiment summary per restaurant.
=== 老王川菜 (Rating: 4.3, Authenticity: 92%) ===
👍 Taste: Mapo Tofu (must-order, 38 mentions), 回锅肉 (authentic, 25 mentions)
👎 Taste: 水煮鱼 (bland, 7 complaints)
👍 Environment: Cozy but cramped (expect wait at peak)
👎 Service: Slow during dinner rush (12 mentions)
💰 Value: ¥65/person, "great value" (22 mentions vs 3 "overpriced")
Step 6: Personalized Ranking
Input: Sentiment summaries + user dining profile. Scoring formula:
Score = w1 × cuisine_match + w2 × sentiment_score + w3 × value_score + w4 × occasion_fit + w5 × authenticity_bonus
Output: Ranked top 5–10 restaurants with:
- Overall score and breakdown
- Must-order dishes (top 3)
- Avoid dishes (top 3)
- Best time to go (based on wait-time mentions)
- Occasion-fit note (e.g., "Great for date — quiet corner tables")
Step 7: Ordering Guide Generation
Input: Top-ranked restaurant + dish sentiment data. Output: Ready-to-use ordering card:
=== 老王川菜 · 2人点菜指南 ===
预算: ¥120-150
必点 (Must Order):
☑ 麻婆豆腐 ¥28 — 38人推荐，招牌菜
☑ 回锅肉 ¥42 — 25人推荐，正宗
可点 (Recommended):
○ 蒜泥白肉 ¥32 — 18人推荐
避雷 (Skip):
✕ 水煮鱼 ¥88 — 7人评价"不入味"，性价比低
💡 人均参考: ¥60-75
🕐 建议时间: 11:30前或13:30后避开高峰
Logic: Adjust portion recommendations based on party size. For group dinners, suggest "大份" or double-ordering popular dishes.
Step 8: Optional — Exploration Route
Input: Multiple restaurants worth visiting + starting location. Output: Geographically optimized route:
🍽️ 静安寺美食探店路线 (步行可达):
1. 老王川菜 (午餐, 11:30) → 步行3分钟
2. 弄堂小馄饨 (下午茶, 14:00) → 步行5分钟
3. 福1088 (晚餐, 18:00)
Logic: Group restaurants by proximity clusters. Factor in operating hours (some close between lunch and dinner).
Sample Prompts
Prompt 1: Quick Recommendation
User: "上海静安寺附近，想吃川菜，人均100左右，约会，推荐3家靠谱的" Expected Output: 3 ranked restaurants with authenticity scores, must-order dishes, date-suitability notes.
Prompt 2: Suspicious Restaurant Check
User: "这家网红店评分4.8但我觉得不好吃，帮我看看评价是不是刷的 —— [Dianping URL]" Expected Output: Fake review detection report: what % of reviews look fake, specific suspicious patterns found, genuine review sentiment summary.
Prompt 3: Dish-Level Deep Dive
User: "今晚去吃XX火锅，帮我看看哪些菜必点，哪些是雷" Expected Output: Detailed ordering guide with specific dish recommendations sourced from genuine reviews.
Prompt 4: Polarized Restaurant Decoder
User: "XX餐厅评分3.8，有人狂推有人狂骂，到底是什么情况？" Expected Output: Analysis of both camps: "5-star reviewers love the authentic taste and value; 1-star reviewers complain about slow service and cramped space. Verdict: go for the food, not the ambiance."
Prompt 5: Multi-City Explorer
User: "下周去成都出差3天，住春熙路附近，帮我规划一个探店路线，要正宗川菜和小吃" Expected Output: 3-day exploration route: lunch + dinner + snack recommendations, geographically clustered, with ordering guides.
Prompt 6: Group Dinner Planning
User: "8个人聚餐，北京国贸附近，预算人均150-200，要有包间，适合聊天的" Expected Output: 5 restaurant options with private room availability notes, group-friendly dish combos, per-person budget breakdown.
Real Task Examples
Example 1: Date Night Decision
Scenario: User planning a third date, wants to impress without being too formal. Input: "上海法租界附近，人均200-300，约会，氛围好但不要太正式，最好是西餐或日料" Steps:
- Profile: 法租界, ¥200-300, date, Western/Japanese
- Discovery → 18 candidates, filtered to 12 after radius check
- Review harvesting → 360+ reviews across candidates
- Fake detection → 2 restaurants flagged (>35% suspicious), removed
- Sentiment: focus on "romantic atmosphere" and "not too noisy"
- Ranking: Top 5 with "date vibe" score
- Ordering guides for top 3 Output: "Mr & Mrs Bund (authenticity 95%, romantic terrace), 鮨一 (94%, intimate counter seating), Osteria (91%, cozy Italian)"
Example 2: Tourist in New City
Scenario: First time in Chengdu, wants authentic local food, not tourist traps. Input: "刚到成都，住宽窄巷子附近，想吃地道川菜和小吃，避开网红店，人均50-100" Steps:
- Profile: 宽窄巷子, ¥50-100, solo/casual, authentic
- Discovery → 25 candidates; immediately filter out 4.8+ perfect scores (tourism red flag)
- Review harvesting → focus on reviews from accounts with local dining history
- Fake detection → remove 6 restaurants with review clusters on weekends
- Sentiment: prioritize "本地人""回头客""老店" mentions
- "Hidden gem" bonus: moderate rating (3.8-4.3) but strong genuine sentiment
- Route: 3-restaurant walking route from 宽窄巷子 Output: "陈麻婆豆腐 (老字号, 96% authentic), 小谭豆花 (local favorite, ¥30/person), 洞子口张老二凉粉 (street food gem)"
Example 3: Business Dinner
Scenario: Hosting 6 clients, needs upscale private dining with guaranteed quality. Input: "北京朝阳区，商务宴请6人，人均400-600，要有包间，粤菜或淮扬菜，服务要专业" Steps:
- Profile: 朝阳区, ¥400-600, business, private room, Cantonese/Huaiyang
- Discovery → filter for "包厢""商务宴请" in tags
- Review focus: service quality mentions, private room experience
- Fake detection → high-end restaurants have fewer fake reviews but check for "探店博主" (paid influencer) patterns
- Sentiment: service quality is weighted 2× for business dining
- Dish recommendations: banquet-style combos, wine pairing suggestions
- Booking tips: how far in advance to reserve Output: "利苑酒家 (service excellence, 98% authentic), 大董烤鸭 (impressive presentation, private rooms), 淮扬府 (elegant, excellent service)"
🚀 First-Success Path (3 Steps)
- Step 1: Say "推荐上海静安寺附近3家靠谱的川菜馆，人均100左右"
- Step 2: Review the ranked list with authenticity scores and must-order dishes
- Step 3: Pick a restaurant, get the ready-to-use ordering guide with specific dishes
Boundary Conditions
| Condition | Behavior |
|---|---|
| <50 total reviews for a restaurant | Flag as "insufficient data"; still show but with low-confidence warning |
| All reviews flagged as suspicious | Mark restaurant "⚠️ High fake review risk"; still show genuine sentiment if any |
| User location outside Dianping coverage | Inform; Dianping covers mainland China primarily |
| Cuisine type has <3 candidates | Expand radius or suggest adjacent cuisines |
| Budget too low for area (<¥30/person) | Suggest street food / food court alternatives |
| Restaurant chain with >10 locations | De-duplicate; show 2-3 closest locations |
| Dianping blocks scraping | Fall back to cached data; show staleness timestamp |
| Party size >12 | Suggest banquet/private dining only; filter out small-table venues |
| Occasion = "proposal" or "anniversary" | Add extra weight to environment + service; flag "special occasion" friendly venues |
Error Handling
| Error Code | Scenario | Handling |
|---|---|---|
| E-SCRAPE-BLOCKED | Dianping anti-scraping triggers | Use cached data, flag staleness, retry after cooldown |
| E-INSUFFICIENT-REVIEWS | Restaurant has <30 reviews | Show with "low confidence" badge; limit recommendations |
| E-LOCATION-UNKNOWN | Location string can't be resolved | Ask user for district or landmark clarification |
| E-NO-CANDIDATES | Zero restaurants match criteria | Relax filters iteratively: expand radius → budget → cuisine |
| E-FAKE-OVERWHELM | >60% reviews detected as fake | Strong warning; recommend skipping this restaurant |
| E-PHOTO-PARSE-FAIL | Can't extract photo metadata | Degrade gracefully; skip photo-based fake detection signals |
Security Requirements
- Review data privacy: Scraped reviews used for analysis only; never stored beyond session
- No account impersonation: All scraping is read-only, public-facing data
- Dianping ToS compliance: Respect robots.txt; reasonable rate limiting (1 req/sec)
- Location privacy: City/district level only; never store or expose precise coordinates
- No review manipulation: This tool reads and analyzes; it does NOT post reviews, boost ratings, or interact with Dianping in any write capacity
- Fair use: Analysis results are personal dining decisions; not for commercial competitive intelligence
Implementation
Project Structure
| File | Purpose |
|---|---|
SKILL.md | Full design document (this file) |
skill.json | Skill metadata with script/schema references |
scripts/dianping-guide.py | Main Python CLI — implements all workflow steps |
schemas/input.schema.json | JSON Schema for dining profile and restaurant data |
schemas/output.schema.json | JSON Schema for all output types (authenticity/sentiment/ranking/ordering/route) |
references/signals.json | Fake review detection signal definitions, weights, and thresholds |
CLI Usage
# Step 1: Define dining context
./scripts/dianping-guide.py profile --location "上海静安寺" --cuisine 川菜 --budget-max 100
# Step 2: Discover candidates (uses built-in sample data)
./scripts/dianping-guide.py discover --cuisine 川菜 --budget-max 100
# Step 3+4+5: Analyze a restaurant (fake detection + sentiment)
./scripts/dianping-guide.py analyze "老王川菜"
# Step 6: Personalized ranking
./scripts/dianping-guide.py rank --cuisine 川菜 --occasion date
# Step 7: Ordering guide
./scripts/dianping-guide.py order "老王川菜" --party-size 2
# Step 8: Exploration route
./scripts/dianping-guide.py route --start "静安寺"
Key Algorithms
- Fake Review Detection: 8-signal ensemble scoring — time clusters, reviewer age, posting frequency, text similarity (cosine), photo EXIF, generic language, rating patterns, reviewer history. Each signal weighted and normalized to 0-100 authenticity index.
- Sentiment Extraction: Chinese keyword-based matching across 4 dimensions (taste/environment/service/value). Dish-level extraction via pattern matching near sentiment keywords.
- Personalized Ranking: Weighted formula
0.25×cuisine_match + 0.20×value + 0.20×occasion + 0.30×authenticity_bonus. - Ordering Guide: Separates dishes into Must-order / Recommended / Skip based on aggregated dish-level sentiment from genuine reviews.
Dependencies
- Python 3.8+ (stdlib only — no external packages required)
references/signals.jsonloaded at runtime for detection thresholds
All analysis uses built-in sample restaurant data. For production use, replace SAMPLE_RESTAURANTS with scraped Dianping data (respecting rate limits and robots.txt).