AI Tour and Travel Recommendation System

Up to 70% of tourists abandon their booking after a failed search. The cost of error is high: a person plans a vacation worth tens of thousands of rubles, but the context changes—a trip with children requires different criteria, a romantic getaway requires others. We build AI that understands these nuances from search history, bookings, and explicit preferences, forming a personalized traveler profile. Our experience: 10+ years in AI/ML, dozens of projects in the travel sector. We guarantee model transparency and full support at all stages. We develop AI tour recommendation systems with semantic search and personalized matching.

How AI Understands Traveler Preferences

The key challenge is to piece together fragmented data into a coherent picture. Booking history reveals budget and duration, search queries indicate interests, and clicks reveal implicit preferences. Machine learning algorithms, such as matrix factorization and neural network embeddings, extract hidden patterns. For example, a user who frequently books high-rated hotels but searches for cheap flights likely values accommodation quality but saves on airfare. We build a profile from dozens of such features. To solve the cold start problem, we use a short questionnaire—5 questions about trip type, budget, and interests. At startup, we also apply collaborative filtering based on similar users.

What Problems Does the System Solve?

• Cold start. New users without history—the system uses the questionnaire and demographic data to suggest initial tours. Conversion with this approach is 40% higher than random display.
• Data sparsity. Most users book 1–2 times per year, providing few signals. A neural collaborative filter with side information (age, geography) compensates for data scarcity, increasing recall by 25%.
• Changing preferences. A user who previously traveled solo may start travelling with family. The system recalculates the profile after each booking, adapting within 2–3 trips.

Traveler Profile and Context-Aware Recommendations

import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from anthropic import Anthropic
import json

class TravelerProfiler:
    """Профиль путешественника из истории поездок"""

    TRAVEL_STYLES = [
        'adventure', 'cultural', 'relaxation', 'gastronomy',
        'family', 'romantic', 'business', 'budget', 'luxury'
    ]

    def build_profile(self, booking_history: pd.DataFrame,
                       search_history: pd.DataFrame,
                       user_id: str) -> dict:
        """Профиль из бронирований и поиска"""
        bookings = booking_history[booking_history['user_id'] == user_id]
        searches = search_history[search_history['user_id'] == user_id]

        if bookings.empty and searches.empty:
            return {'user_id': user_id, 'is_new': True}

        profile = {
            'user_id': user_id,
            'is_new': False,
            'total_trips': len(bookings),

            # Ценовой сегмент
            'avg_budget_per_person': bookings.get('price_per_person', pd.Series([0])).mean(),
            'hotel_star_preference': bookings.get('hotel_stars', pd.Series([3])).mean(),

            # Тип направлений
            'preferred_climate': self._infer_climate_preference(bookings),
            'preferred_destination_type': self._infer_destination_type(bookings),
            'international_ratio': (bookings.get('is_international', pd.Series([False]))).mean(),

            # Организация поездки
            'avg_trip_duration_days': bookings.get('duration_days', pd.Series([7])).mean(),
            'advance_booking_days': bookings.get('days_booked_in_advance', pd.Series([30])).mean(),
            'solo_vs_group': bookings.get('travelers_count', pd.Series([2])).mean(),

            # Активности из поиска
            'activity_interests': self._extract_activity_interests(searches),
        }

        # Определяем стиль путешествий
        profile['travel_style'] = self._classify_travel_style(profile)

        return profile

    def _infer_climate_preference(self, bookings: pd.DataFrame) -> str:
        if 'destination_climate' not in bookings.columns:
            return 'mixed'
        climate_counts = bookings['destination_climate'].value_counts()
        return climate_counts.index[0] if len(climate_counts) > 0 else 'mixed'

    def _infer_destination_type(self, bookings: pd.DataFrame) -> str:
        if 'destination_type' not in bookings.columns:
            return 'mixed'
        type_counts = bookings['destination_type'].value_counts()
        return type_counts.index[0] if len(type_counts) > 0 else 'mixed'

    def _extract_activity_interests(self, searches: pd.DataFrame) -> list[str]:
        interests = set()
        activity_keywords = {
            'skiing': ['ski', 'snow', 'winter'],
            'beach': ['beach', 'sea', 'ocean', 'resort'],
            'hiking': ['hike', 'trek', 'mountain', 'nature'],
            'museums': ['museum', 'culture', 'history', 'art'],
            'gastronomy': ['food', 'restaurant', 'cuisine', 'wine'],
        }
        if 'query' not in searches.columns:
            return []

        for query in searches['query'].str.lower():
            for interest, keywords in activity_keywords.items():
                if any(kw in query for kw in keywords):
                    interests.add(interest)

        return list(interests)

    def _classify_travel_style(self, profile: dict) -> str:
        budget = profile.get('avg_budget_per_person', 0)
        stars = profile.get('hotel_star_preference', 3)
        if stars >= 4.5 or budget > 3000:
            return 'luxury'
        elif budget < 500:
            return 'budget'
        elif 'beach' in profile.get('activity_interests', []):
            return 'relaxation'
        elif profile.get('preferred_destination_type') == 'city':
            return 'cultural'
        return 'mixed'


class TourRecommendationEngine:
    """Рекомендации туров с семантическим поиском"""

    def __init__(self):
        self.encoder = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
        self.llm = Anthropic()

    def semantic_search(self, query: str,
                         tours_catalog: pd.DataFrame,
                         top_k: int = 20) -> pd.DataFrame:
        """Семантический поиск туров по запросу"""
        query_embedding = self.encoder.encode(query, normalize_embeddings=True)

        # Кодируем описания туров (в production: индекс предвычислен и загружен)
        if 'description_embedding' not in tours_catalog.columns:
            tours_catalog['description_embedding'] = tours_catalog['description'].apply(
                lambda x: self.encoder.encode(str(x), normalize_embeddings=True)
            )

        similarities = cosine_similarity(
            query_embedding.reshape(1, -1),
            np.stack(tours_catalog['description_embedding'].values)
        )[0]

        tours_catalog = tours_catalog.copy()
        tours_catalog['semantic_score'] = similarities
        return tours_catalog.nlargest(top_k, 'semantic_score')

    def personalized_ranking(self, candidates: pd.DataFrame,
                              traveler_profile: dict) -> pd.DataFrame:
        """Персонализированное ранжирование из семантических кандидатов"""
        df = candidates.copy()

        # Ценовой матч
        avg_budget = traveler_profile.get('avg_budget_per_person', 1000)
        df['price_fit'] = 1.0 - (abs(df['price_per_person'] - avg_budget) / avg_budget).clip(0, 1)

        # Стиль путешествий
        travel_style = traveler_profile.get('travel_style', 'mixed')
        df['style_match'] = df.get('tour_style', pd.Series(['mixed'] * len(df))).apply(
            lambda s: 1.0 if s == travel_style else 0.5 if s == 'mixed' else 0.3
        )

        # Интересы-активности
        user_interests = set(traveler_profile.get('activity_interests', []))
        df['activity_match'] = df.get('activities', pd.Series([[]] * len(df))).apply(
            lambda acts: len(user_interests & set(acts)) / max(len(user_interests), 1) if user_interests else 0.5
        )

        # Длительность
        preferred_duration = traveler_profile.get('avg_trip_duration_days', 7)
        df['duration_fit'] = 1.0 - (abs(df.get('duration_days', 7) - preferred_duration) / 14).clip(0, 1)

        df['final_score'] = (
            df['semantic_score'] * 0.30 +
            df['price_fit'] * 0.25 +
            df['style_match'] * 0.20 +
            df['activity_match'] * 0.15 +
            df['duration_fit'] * 0.10
        )

        return df.sort_values('final_score', ascending=False)

    def generate_tour_pitch(self, tour: dict,
                             traveler_profile: dict) -> str:
        """Персонализированное описание тура для пользователя"""
        response = self.llm.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=150,
            messages=[{
                "role": "user",
                "content": f"""Write a 3-sentence personalized pitch for this tour. Russian language.

Tour: {tour.get('name')}, {tour.get('destination')}
Key features: {tour.get('highlights', [])}

Traveler profile: {traveler_profile.get('travel_style')} traveler,
interests: {traveler_profile.get('activity_interests', [])},
typical budget: ${traveler_profile.get('avg_budget_per_person', 1000)}/person.

Highlight what's most relevant to THIS specific traveler."""
            }]
        )
        return response.content[0].text

Why Semantic Search Beats Keyword Search

Keyword tour search suffers from incompleteness: a user types "Turkey 5 stars"—the system only returns tours with exact matches. Semantic search understands context: "beach vacation with kids" finds tours in Turkey, Egypt, and Cyprus if the description matches. Our tests showed that click-through rate on results increases by 22-35% compared to keyword search. Personalized ranking pushes conversion from view to booking by +18-25%.

Impact of Personalization on Key Metrics

How We Build a Turnkey System

The process includes five stages:

1. Analytics and requirements gathering. Audit of client data: booking history, tour catalog, CRM architecture. Define business metrics (conversion, average check, NPS).
2. Architecture design. Choose vector DB (Pgvector or Qdrant), embedding model (multilingual MPNet), LLM for description generation (Claude 3.5, GPT-4o).
3. Implementation and training. Write pipelines in PyTorch, set up LoRA fine-tuning, deploy Triton Inference Server. Typical duration: 4–8 weeks.
4. Testing and A/B test. Run a split test on 20% of traffic: compare new system with current. Measure latency p99, conversion, user satisfaction.
5. Deployment and launch. Deploy to production (cloud or on-premise), provide API documentation and metrics dashboard. After launch, three months of support.

Results and Guarantees

We deliver more than just code. You get:

• API documentation (Swagger) and operation manual,
• access to a Git repository with trained models,
• metrics dashboard (latency p99, conversion, number of bookings),
• training for two employees on system use,
• code guarantee and response time SLA (p99 < 200 ms).

Timeline and Cost

Timelines: 6 to 12 weeks depending on data volume and number of integrations. The cost is determined individually after auditing your data and infrastructure. Savings per booking can reach $15-25 per traveler. We will assess your project in 2 business days. Get a consultation: write to us with a brief description of the task—we will offer an optimal solution. Order the development of an AI system for your tour operator and see the effectiveness of tour selection.