Duplicate from Kratos-AI/Food_Delivery_App_Screen_Recording_Dataset

Co-authored-by: KAI <[email protected]>

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README.md

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+---
+license: cc-by-4.0
+pretty_name: 'Food Delivery App Screen Recording Dataset'
+language:
+- en
+tags:
+- screen-recording
+- food-delivery
+- mobile-app
+- user-interface
+- clickstream
+- usability
+- ai-research
+- video-to-text
+- ui-analysis
+task_categories:
+- video-classification
+- video-text-to-text
+size_categories:
+- 1K<n<10K
+---
+
+# Food Delivery App Screen Recording Dataset
+*This dataset contains high-quality screen recordings of user interactions on food delivery applications. 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:  
+  - [email protected]  
+  - [email protected]  
+
+## Supported Tasks
+
+- **Task Categories**:  
+  - Video Classification  
+  - Video-to-Text Generation  
+
+- **Supported Tasks**:  
+  - Automatic captioning of food ordering workflows  
+  - User journey segmentation (browse → select → checkout → track)  
+  - Action recognition (e.g., restaurant search, menu navigation, cart update, payment)  
+  - UX pattern analysis for app usability  
+  - Behavioral modeling for personalized recommendations  
+  - Multimodal research in mobile commerce  
+
+## Languages
+
+- **Primary Language**: English (UI, restaurant listings, menus, and user interactions)  
+
+## Dataset Creation
+
+### Curation Rationale
+This dataset was created to advance research in mobile commerce, user experience analysis, and AI systems that understand food delivery workflows. It enables training models that capture step-by-step ordering journeys, user preferences, and decision-making patterns.
+
+### Source Data
+- **Contributors**: Simulated user sessions on food delivery apps  
+- **Collection Process**: Screen recordings of diverse user activities including browsing restaurants, selecting dishes, applying filters, customizing orders, making payments, and tracking deliveries. All personally identifiable information (PII) has been anonymized or removed.  
+
+### Other Known Limitations
+- **Size**: Dataset may not include all features (e.g., cancellations, refunds, customer support chats)  
+- **Bias**: Sessions may overrepresent certain cuisines, price ranges, or user behaviors  
+- **Platform-Specificity**: May not generalize across all food delivery apps or regional platforms  
+
+## Intended Uses
+
+### ✅ Direct Use
+- Training AI models for action recognition in app workflows  
+- Research in human-computer interaction (HCI) and usability testing  
+- Automatic summarization of food ordering journeys  
+- Academic research in mobile commerce, UX, and behavioral AI  
+
+### ❌ Out-of-Scope Use
+- Tracking or surveillance of real users without explicit consent  
+- Commercial exploitation without attribution or ethical clearance  
+- Extraction of sensitive payment or personal details  
+- Real-time monitoring or decision-making without human oversight  
+
+## License
+
+CC BY 4.0

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