DistilBERT User Profiling and matching Model (developer not writer)

To fully implement the architecture for the three models—profile creation, event creation, and matching events with user profiles—on devices using DistilBERT, let's detail each component, including the practical data flow and model interactions. This structure will ensure privacy, efficiency, and adaptability of the system in real-world scenarios. 1. Profile Creation Model Architecture: Input Layer: Takes raw user data including textual descriptions and user activity logs. Processing Layer: Utilizes DistilBERT to extract features such as interests, professional background, and preferences from textual data. Output Layer: Produces a structured user profile with categorized data such as interests (e.g., AI, outdoor activities), professional background (e.g., software developer), and preferred event types (e.g., workshops). Example: Input: "I am a web developer with a keen interest in blockchain technologies. I enjoy outdoor sports and networking events." Processing: DistilBERT Analysis: Extracts "web developer" as a profession, "blockchain" as an interest under technology, and "outdoor sports" and "networking events" as social preferences. Output: Profile: { Profession: "Web Developer", Interests: ["Blockchain", "Outdoor Sports"], Event Preferences: ["Networking Events"], Social Preferences: ["Outdoor Activities"] } 2. Event Creation Model Architecture: Input Layer: Accepts raw event descriptions and other metadata provided by the event organizers. Processing Layer: DistilBERT processes the description to categorize the event and tag it with relevant attributes like location, event type, and key topics. i have the datasets as well Output Layer: Generates a structured event profile that is stored locally and used for matching with user profiles. Example: Input: "Explore the future of AI at our annual conference with interactive sessions in Silicon Valley this August." Processing: DistilBERT Analysis: Identifies "AI" as the key topic, "annual conference" as the event type, and "Silicon Valley" as the location. Output: Event Profile: { Category: "Technology", Type: "Conference", Location: "Silicon Valley", Keywords: ["AI", "Interactive Sessions"], Time: "August" } 3. Matching Events with User Profiles Architecture: Input Layer: Receives structured profiles of both users and events from their respective local databases. Processing Layer: A similarity calculation algorithm (using techniques such as cosine similarity or a machine-learned scoring model) evaluates the fit between user preferences and event attributes. Output Layer: Provides a list of event recommendations ranked by relevance to the user’s profile. Example: Input: User Profile { Profession: "Web Developer", Interests: ["Blockchain", "Outdoor Sports"], Event Preferences: ["Networking Events"], Location: "San Francisco" } Event Profile { Category: "Technology", Type: "Conference", Location: "Silicon Valley", Keywords: ["AI", "Interactive Sessions"], Time: "August" } Processing: Similarity Score: Calculate how well the event's features match the user’s interests and preferences. Output: Recommendation: High relevance due to the match in professional interest (technology) and geographical proximity. Implementation Considerations Local Processing and Storage: All three components operate entirely on-device, ensuring data privacy and reducing latency. Optimized DistilBERT: Employ techniques such as quantization and pruning to ensure the model runs efficiently on mobile devices. Incremental Learning and Updates: Models can be incrementally updated with new data inputs to refine their outputs continuously, using techniques like online learning. Feedback System: Incorporate user feedback to adapt and improve model predictions and relevance over time. This architecture ensures a cohesive and interactive user experience, allowing for dynamic event discovery and networking opportunities tailored to individual preferences, all while maintaining a high standard of privacy and data security.