76a118ddbf
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Backend: - Add YieldDomain, YieldTransaction, YieldPayout, AffiliatePartner models - Create IntentDetector service for keyword-based intent classification - Implement /api/v1/yield/* endpoints (dashboard, domains, transactions, partners) - Support domain activation, DNS verification, and revenue tracking Frontend: - Add /terminal/yield page with dashboard and activate wizard - Add YIELD to sidebar navigation under 'Monetize' section - Add 4th pillar 'Yield' to landing page 'Beyond Hunting' section - Extend API client with yield endpoints and types Features: - AI-powered intent detection (medical, finance, legal, realestate, etc.) - Swiss/German geo-targeting with city recognition - Revenue estimation based on intent category and geo - DNS verification via nameservers or CNAME - 70/30 revenue split tracking
498 lines
16 KiB
Python
498 lines
16 KiB
Python
"""
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Intent Detection Engine for Yield Domains.
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Analyzes domain names to detect user intent and match with affiliate partners.
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Uses keyword matching, pattern detection, and NLP-lite techniques.
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"""
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import re
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from dataclasses import dataclass
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from typing import Optional
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@dataclass
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class IntentResult:
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"""Result of intent detection for a domain."""
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category: str # Primary intent category
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subcategory: Optional[str] # More specific subcategory
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confidence: float # 0.0 - 1.0
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keywords_matched: list[str] # Which keywords triggered the match
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suggested_partners: list[str] # Affiliate partner slugs
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monetization_potential: str # "high", "medium", "low"
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# Intent categories with keywords (Swiss/German/English focus)
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INTENT_PATTERNS = {
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# Medical / Health
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"medical_dental": {
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"keywords": [
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"zahnarzt", "dental", "dentist", "zahn", "zähne", "kieferorthopäde",
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"implantate", "zahnklinik", "prothese", "bleaching", "zahnpflege",
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"dentalhygiene", "mundgesundheit", "braces", "orthodontist"
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],
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"patterns": [r"zahn\w*", r"dent\w*"],
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"potential": "high",
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"partners": ["comparis_dental", "swisssmile", "dentaldeal"]
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},
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"medical_general": {
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"keywords": [
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"arzt", "doctor", "klinik", "clinic", "hospital", "spital",
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"praxis", "gesundheit", "health", "medizin", "medicine",
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"therapie", "therapy", "behandlung", "treatment"
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],
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"patterns": [r"med\w+", r"gesund\w*", r"health\w*"],
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"potential": "high",
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"partners": ["comparis_health", "sanitas", "helsana"]
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},
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"medical_beauty": {
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"keywords": [
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"schönheit", "beauty", "kosmetik", "cosmetic", "botox",
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"filler", "laser", "aesthetic", "ästhetik", "haut", "skin",
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"anti-aging", "wellness", "spa", "massage"
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],
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"patterns": [r"beauty\w*", r"kosm\w*"],
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"potential": "high",
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"partners": ["swissesthetic", "beautyfinder"]
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},
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# Finance / Insurance
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"finance_insurance": {
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"keywords": [
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"versicherung", "insurance", "krankenkasse", "autoversicherung",
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"hausrat", "haftpflicht", "lebensversicherung", "police"
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],
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"patterns": [r"versicher\w*", r"insur\w*"],
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"potential": "high",
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"partners": ["comparis_insurance", "bonus_ch", "financescout"]
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},
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"finance_mortgage": {
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"keywords": [
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"hypothek", "mortgage", "kredit", "credit", "darlehen", "loan",
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"finanzierung", "financing", "immobilien", "eigenheim"
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],
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"patterns": [r"hypo\w*", r"kredit\w*", r"mortg\w*"],
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"potential": "high",
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"partners": ["comparis_hypo", "moneypark", "hypocenter"]
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},
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"finance_banking": {
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"keywords": [
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"bank", "banking", "konto", "account", "sparen", "savings",
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"anlegen", "invest", "geld", "money", "zinsen", "interest"
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],
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"patterns": [r"bank\w*", r"finanz\w*"],
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"potential": "medium",
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"partners": ["neon_bank", "yuh_ch"]
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},
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# Legal
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"legal_general": {
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"keywords": [
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"anwalt", "lawyer", "rechtsanwalt", "attorney", "rechtshilfe",
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"legal", "recht", "law", "kanzlei", "advokat", "jurist"
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],
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"patterns": [r"anwalt\w*", r"recht\w*", r"law\w*"],
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"potential": "high",
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"partners": ["legal_ch", "anwalt24"]
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},
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# Real Estate
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"realestate_buy": {
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"keywords": [
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"immobilien", "realestate", "wohnung", "apartment", "haus", "house",
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"kaufen", "buy", "villa", "eigentum", "property", "liegenschaft"
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],
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"patterns": [r"immobil\w*", r"wohn\w*"],
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"potential": "high",
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"partners": ["homegate", "immoscout", "comparis_immo"]
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},
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"realestate_rent": {
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"keywords": [
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"mieten", "rent", "miete", "mietwohnung", "rental", "wg",
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"studio", "loft", "untermiete"
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],
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"patterns": [r"miet\w*", r"rent\w*"],
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"potential": "medium",
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"partners": ["homegate", "flatfox"]
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},
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# Travel
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"travel_flights": {
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"keywords": [
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"flug", "flight", "fliegen", "fly", "airline", "flughafen",
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"airport", "billigflug", "cheapflight", "reise", "travel"
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],
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"patterns": [r"fl[uy]g\w*", r"travel\w*"],
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"potential": "medium",
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"partners": ["skyscanner", "kayak", "booking"]
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},
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"travel_hotels": {
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"keywords": [
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"hotel", "unterkunft", "accommodation", "hostel", "pension",
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"resort", "übernachtung", "booking", "airbnb"
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],
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"patterns": [r"hotel\w*"],
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"potential": "medium",
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"partners": ["booking_com", "trivago", "hotels_com"]
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},
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# E-Commerce / Shopping
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"shopping_general": {
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"keywords": [
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"shop", "store", "kaufen", "buy", "einkaufen", "shopping",
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"deals", "rabatt", "discount", "sale", "angebot", "offer"
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],
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"patterns": [r"shop\w*", r"deal\w*"],
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"potential": "medium",
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"partners": ["amazon_ch", "galaxus", "digitec"]
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},
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"shopping_fashion": {
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"keywords": [
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"mode", "fashion", "kleider", "clothes", "schuhe", "shoes",
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"outfit", "style", "bekleidung", "garderobe"
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],
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"patterns": [r"mode\w*", r"fash\w*"],
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"potential": "medium",
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"partners": ["zalando", "about_you"]
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},
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# Automotive
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"auto_buy": {
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"keywords": [
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"auto", "car", "fahrzeug", "vehicle", "wagen", "neuwagen",
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"gebrauchtwagen", "occasion", "carmarket", "autohaus"
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],
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"patterns": [r"auto\w*", r"car\w*"],
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"potential": "high",
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"partners": ["autoscout", "comparis_auto", "carforyou"]
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},
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"auto_service": {
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"keywords": [
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"garage", "werkstatt", "reparatur", "repair", "service",
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"reifenwechsel", "inspektion", "tuning"
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],
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"patterns": [r"garag\w*"],
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"potential": "medium",
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"partners": ["autobutler"]
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},
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# Jobs / Career
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"jobs": {
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"keywords": [
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"job", "jobs", "karriere", "career", "arbeit", "work",
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"stelle", "stellenangebot", "vacancy", "hiring", "bewerbung"
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],
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"patterns": [r"job\w*", r"karrier\w*"],
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"potential": "medium",
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"partners": ["jobs_ch", "indeed", "linkedin"]
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},
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# Education
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"education": {
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"keywords": [
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"schule", "school", "uni", "university", "bildung", "education",
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"kurs", "course", "lernen", "learn", "ausbildung", "training",
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"weiterbildung", "studium", "studieren"
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],
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"patterns": [r"schul\w*", r"edu\w*", r"learn\w*"],
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"potential": "medium",
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"partners": ["udemy", "coursera", "edx"]
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},
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# Technology
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"tech_hosting": {
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"keywords": [
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"hosting", "server", "cloud", "domain", "website", "webhosting",
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"vps", "dedicated", "webspace"
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],
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"patterns": [r"host\w*", r"server\w*"],
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"potential": "medium",
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"partners": ["hostpoint", "infomaniak", "cyon"]
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},
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"tech_software": {
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"keywords": [
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"software", "app", "tool", "saas", "crm", "erp",
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"programm", "application", "platform"
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],
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"patterns": [r"soft\w*", r"app\w*"],
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"potential": "medium",
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"partners": ["capterra", "g2"]
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},
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# Food / Restaurant
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"food_restaurant": {
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"keywords": [
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"restaurant", "essen", "food", "pizza", "sushi", "burger",
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"cafe", "bistro", "gastronomie", "dining"
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],
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"patterns": [r"food\w*", r"pizza\w*"],
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"potential": "low",
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"partners": ["eatme", "uber_eats"]
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},
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"food_delivery": {
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"keywords": [
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"lieferung", "delivery", "liefern", "bestellen", "order",
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"takeaway", "takeout"
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],
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"patterns": [r"deliver\w*", r"liefer\w*"],
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"potential": "medium",
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"partners": ["uber_eats", "just_eat"]
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},
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}
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# Swiss city names for geo-targeting
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SWISS_CITIES = {
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"zürich", "zurich", "zuerich", "zri", "zh",
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"bern", "genf", "geneva", "geneve",
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"basel", "lausanne", "luzern", "lucerne",
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"winterthur", "stgallen", "st-gallen", "lugano",
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"biel", "bienne", "thun", "köniz", "chur",
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"schaffhausen", "fribourg", "freiburg",
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"neuchatel", "neuenburg", "uster", "sion", "sitten",
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"zug", "aarau", "baden", "wil", "davos", "interlaken"
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}
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# German cities
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GERMAN_CITIES = {
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"berlin", "münchen", "munich", "muenchen", "hamburg",
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"frankfurt", "köln", "koeln", "düsseldorf", "duesseldorf",
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"stuttgart", "dortmund", "essen", "leipzig", "bremen"
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}
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class IntentDetector:
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"""
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Detects user intent from domain names.
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Uses keyword matching and pattern detection to categorize domains
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and suggest appropriate affiliate partners for monetization.
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"""
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def __init__(self):
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self.patterns = INTENT_PATTERNS
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self.swiss_cities = SWISS_CITIES
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self.german_cities = GERMAN_CITIES
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def detect(self, domain: str) -> IntentResult:
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"""
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Analyze a domain name and detect its intent category.
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Args:
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domain: The domain name (e.g., "zahnarzt-zuerich.ch")
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Returns:
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IntentResult with category, confidence, and partner suggestions
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"""
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# Normalize domain
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domain_clean = self._normalize_domain(domain)
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parts = self._split_domain_parts(domain_clean)
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# Find best matching category
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best_match = None
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best_score = 0.0
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best_keywords = []
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for category, config in self.patterns.items():
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score, matched_keywords = self._score_category(parts, config)
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if score > best_score:
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best_score = score
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best_match = category
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best_keywords = matched_keywords
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# Determine confidence level
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confidence = min(best_score / 3.0, 1.0) # Normalize to 0-1
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# If no strong match, return generic
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if best_score < 0.5 or best_match is None:
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return IntentResult(
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category="generic",
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subcategory=None,
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confidence=0.2,
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keywords_matched=[],
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suggested_partners=["generic_affiliate"],
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monetization_potential="low"
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)
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# Get category config
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config = self.patterns[best_match]
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# Split category into main and sub
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parts = best_match.split("_", 1)
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main_category = parts[0]
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subcategory = parts[1] if len(parts) > 1 else None
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return IntentResult(
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category=main_category,
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subcategory=subcategory,
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confidence=confidence,
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keywords_matched=best_keywords,
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suggested_partners=config.get("partners", []),
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monetization_potential=config.get("potential", "medium")
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)
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def detect_geo(self, domain: str) -> Optional[str]:
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"""
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Detect geographic targeting from domain name.
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Returns:
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ISO country code if detected (e.g., "CH", "DE"), None otherwise
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"""
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domain_clean = self._normalize_domain(domain)
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parts = set(self._split_domain_parts(domain_clean))
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# Check TLD first
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if domain.endswith(".ch") or domain.endswith(".swiss"):
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return "CH"
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if domain.endswith(".de"):
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return "DE"
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if domain.endswith(".at"):
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return "AT"
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# Check city names
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if parts & self.swiss_cities:
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return "CH"
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if parts & self.german_cities:
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return "DE"
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return None
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def estimate_value(self, domain: str) -> dict:
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"""
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Estimate the monetization value of a domain.
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Returns dict with value estimates based on intent and traffic potential.
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"""
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intent = self.detect(domain)
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geo = self.detect_geo(domain)
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# Base value by potential
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base_values = {
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"high": {"min": 50, "max": 500},
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"medium": {"min": 20, "max": 100},
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"low": {"min": 5, "max": 30}
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}
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potential = intent.monetization_potential
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base = base_values.get(potential, base_values["low"])
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# Adjust for geo (Swiss = premium)
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multiplier = 1.5 if geo == "CH" else 1.0
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# Adjust for confidence
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confidence_mult = 0.5 + (intent.confidence * 0.5)
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return {
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"estimated_monthly_min": int(base["min"] * multiplier * confidence_mult),
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"estimated_monthly_max": int(base["max"] * multiplier * confidence_mult),
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"currency": "CHF" if geo == "CH" else "EUR",
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"potential": potential,
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"confidence": intent.confidence,
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"geo": geo
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}
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def _normalize_domain(self, domain: str) -> str:
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"""Remove TLD and normalize domain string."""
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# Remove common TLDs
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domain = re.sub(r'\.(com|net|org|ch|de|at|io|co|info|swiss)$', '', domain.lower())
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# Replace common separators with space
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domain = re.sub(r'[-_.]', ' ', domain)
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return domain.strip()
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def _split_domain_parts(self, domain_clean: str) -> list[str]:
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"""Split domain into meaningful parts."""
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# Split on spaces (from separators)
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parts = domain_clean.split()
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# Also try to split camelCase or compound words
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expanded = []
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for part in parts:
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# Try to find compound word boundaries
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expanded.append(part)
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# Add any sub-matches for longer words
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if len(part) > 6:
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expanded.extend(self._find_subwords(part))
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return expanded
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def _find_subwords(self, word: str) -> list[str]:
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"""Find meaningful subwords in compound words."""
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subwords = []
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# Check if any keywords are contained in this word
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for config in self.patterns.values():
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for keyword in config["keywords"]:
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if keyword in word and keyword != word:
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subwords.append(keyword)
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return subwords
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def _score_category(self, parts: list[str], config: dict) -> tuple[float, list[str]]:
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"""
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Score how well domain parts match a category.
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Returns (score, matched_keywords)
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"""
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score = 0.0
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matched = []
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keywords = set(config.get("keywords", []))
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patterns = config.get("patterns", [])
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for part in parts:
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# Exact keyword match
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if part in keywords:
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score += 1.0
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matched.append(part)
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continue
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# Partial keyword match
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for kw in keywords:
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if kw in part or part in kw:
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score += 0.5
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matched.append(f"{part}~{kw}")
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break
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# Regex pattern match
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for pattern in patterns:
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if re.match(pattern, part):
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score += 0.7
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matched.append(f"{part}@{pattern}")
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break
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return score, matched
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# Singleton instance
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_detector = None
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def get_intent_detector() -> IntentDetector:
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"""Get singleton IntentDetector instance."""
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global _detector
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if _detector is None:
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_detector = IntentDetector()
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return _detector
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def detect_domain_intent(domain: str) -> IntentResult:
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"""Convenience function to detect intent for a domain."""
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return get_intent_detector().detect(domain)
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def estimate_domain_yield(domain: str) -> dict:
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"""Convenience function to estimate yield value for a domain."""
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detector = get_intent_detector()
|
|
intent = detector.detect(domain)
|
|
value = detector.estimate_value(domain)
|
|
|
|
return {
|
|
"domain": domain,
|
|
"intent": {
|
|
"category": intent.category,
|
|
"subcategory": intent.subcategory,
|
|
"confidence": intent.confidence,
|
|
"keywords": intent.keywords_matched
|
|
},
|
|
"value": value,
|
|
"partners": intent.suggested_partners,
|
|
"monetization_potential": intent.monetization_potential
|
|
}
|
|
|