Duplicate from Kratos-AI/Hotel_Booking_Screen_Recording_Dataset

Co-authored-by: KAI <KAI-KratosAI@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+pretty_name: 'Hotel Booking Screen Recording Dataset'
+language:
+- en
+tags:
+- screen-recording
+- hotel-booking
+- travel
+- user-interface
+- clickstream
+- usability
+- ai-research
+- video-to-text
+- ui-analysis
+task_categories:
+- video-classification
+- video-text-to-text
+size_categories:
+- 1K<n<10K
+---
+
+# Hotel Booking Screen Recording Dataset
+*This dataset contains high-quality screen recordings of user interactions on hotel booking platforms. It has been carefully curated, cleaned, and anonymized to ensure accuracy, completeness, and compliance with privacy standards, making it suitable for AI training, UX research, and user behavior analysis.*
+
+## Contact
+For queries or collaborations related to this dataset, contact:  
+  - anoushka@kgen.io  
+  - abhishek.vadapalli@kgen.io  
+
+## Supported Tasks
+
+- **Task Categories**:  
+  - Video Classification  
+  - Video-to-Text Generation  
+
+- **Supported Tasks**:  
+  - Automatic captioning of hotel booking workflows  
+  - User journey segmentation (search, filter, booking, payment)  
+  - Action recognition (e.g., date selection, room choice, checkout)  
+  - UX pattern analysis for booking flows  
+  - Behavioral modeling for recommendation systems  
+  - Travel AI research (search intent, price sensitivity, booking patterns)  
+
+## Languages
+
+- **Primary Language**: English (UI, hotel listings, and user interactions)  
+
+## Dataset Creation
+
+### Curation Rationale
+This dataset was created to advance research in digital travel services, user interface analysis, and AI systems for booking workflows. It enables training models that can understand step-by-step hotel booking journeys, user decision-making, and intent patterns.
+
+### Source Data
+- **Contributors**: Simulated user sessions on hotel booking platforms  
+- **Collection Process**: Screen recordings of diverse user activities including browsing hotels, applying filters, comparing prices, selecting rooms, and completing bookings. All personally identifiable information (PII) has been anonymized or removed.  
+
+### Other Known Limitations
+- **Size**: Dataset may not cover all edge cases (e.g., cancellations, customer service interactions)  
+- **Bias**: Recorded sessions may overrepresent certain geographies, hotel types, or booking behaviors  
+- **Platform-Specificity**: May not generalize across all hotel booking platforms or mobile-first apps  
+
+## Intended Uses
+
+### ✅ Direct Use
+- Training AI models for action recognition in booking workflows  
+- Research in human-computer interaction (HCI) and usability testing  
+- Automatic summarization of booking journeys  
+- Academic research in travel-tech UX and decision support systems  
+
+### ❌ Out-of-Scope Use
+- Tracking or surveillance of real users without explicit consent  
+- Commercial exploitation without attribution or ethical clearance  
+- Extraction of personal or sensitive booking details  
+- Real-time decision-making without human oversight  
+
+## License
+
+CC BY 4.0

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