Dianping 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.

text

=== 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:

text

Score = w1 × cuisine_match + w2 × sentiment_score + w3 × value_score + w4 × occasion_fit + w5 × authenticity_bonus

Output: Ranked top 5–10 restaurants with:

1. Overall score and breakdown
2. Must-order dishes (top 3)
3. Avoid dishes (top 3)
4. Best time to go (based on wait-time mentions)
5. 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:

text

=== 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:

text

🍽️ 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:

1. Profile: 法租界, ¥200-300, date, Western/Japanese
2. Discovery → 18 candidates, filtered to 12 after radius check
3. Review harvesting → 360+ reviews across candidates
4. Fake detection → 2 restaurants flagged (>35% suspicious), removed
5. Sentiment: focus on "romantic atmosphere" and "not too noisy"
6. Ranking: Top 5 with "date vibe" score
7. 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:

1. Profile: 宽窄巷子, ¥50-100, solo/casual, authentic
2. Discovery → 25 candidates; immediately filter out 4.8+ perfect scores (tourism red flag)
3. Review harvesting → focus on reviews from accounts with local dining history
4. Fake detection → remove 6 restaurants with review clusters on weekends
5. Sentiment: prioritize "本地人""回头客""老店" mentions
6. "Hidden gem" bonus: moderate rating (3.8-4.3) but strong genuine sentiment
7. 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:

1. Profile: 朝阳区, ¥400-600, business, private room, Cantonese/Huaiyang
2. Discovery → filter for "包厢""商务宴请" in tags
3. Review focus: service quality mentions, private room experience
4. Fake detection → high-end restaurants have fewer fake reviews but check for "探店博主" (paid influencer) patterns
5. Sentiment: service quality is weighted 2× for business dining
6. Dish recommendations: banquet-style combos, wine pairing suggestions
7. 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)

1. 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"
2. Step 2: Review the ranked list with authenticity scores and must-order dishes
3. 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

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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

bash

# 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.json loaded 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).