Future Event Detection Model for Personalized Notifications
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in identifying and accessing relevant future events of interest due to the abundance of information, with existing search methods often returning past events or uninteresting future events, leading to wasted time and computing resources.
Innovation Solution
A future event detection model, trained using part of speech analysis and entity recognition, provides personalized notifications to users based on their interests, interests inferred from user data such as location, social media profiles, and previous activities, allowing for efficient identification of relevant future events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users perform manual search to identify future events, then they can access event information, but they spend extensive time and computing resources searching
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and indexing content items before users need to search. The future event detection model proactively identifies potential future events and stores them in a database, so when users have interest queries, the information is already prepared and can be retrieved instantly without requiring users to manually search through vast amounts of content.
Solution Approach 2:
The system provides self-service by automatically detecting future events, determining user interests, and delivering personalized notifications without requiring user-initiated search actions. The future event detection model autonomously processes content items, and the notification system automatically delivers relevant events to users based on their profiles, eliminating the need for users to manually search and filter information.
2Loss of information
If users perform manual search to identify future events, then they can access event information, but computing resources are wasted
Solution Approach 1:
The system performs preliminary computational actions by continuously processing and indexing content items in advance. The future event detection model pre-identifies potential future events and stores metadata about them, so when users have interest queries, the system can quickly retrieve relevant information without requiring computationally intensive real-time search operations through vast datasets.
Solution Approach 2:
The system extracts only the necessary computational processing by using the future event detection model to pre-identify and separate relevant future events from irrelevant content. Instead of requiring users to manually search through all content items consuming significant computing resources, the system has already extracted and isolated the relevant future event information, making it ready for rapid delivery to interested users.
3Productivity
If users search for future events without personalization, then they receive search results, but the results are not interesting to them
Solution Approach 1:
The system applies local quality by tailoring search results to individual users based on their specific interests and preferences. Instead of providing generic search results to all users, the system analyzes user profiles, historical data, and expressed interests to customize the future event notifications for each user, ensuring that the information delivered is highly relevant and interesting to that specific user.
Solution Approach 2:
The system implements dynamics by continuously adapting and updating user interest profiles based on ongoing user behavior and feedback. The future event detection model dynamically adjusts its recommendations based on changing user preferences, ensuring that the personalized notifications remain relevant over time. The system learns from user interactions and automatically refines its understanding of user interests.
4Loss of information
If users repeatedly resubmit search queries to obtain fresh results, then they can access updated event information, but they must do so on hourly, daily, and weekly bases
Solution Approach 1:
The system provides continuity of useful action by continuously monitoring and indexing new content items as they are published. The future event detection model operates continuously, automatically detecting and adding new future events to the database without interruption. This continuous operation ensures that users always have access to the most up-to-date event information without needing to repeatedly resubmit search queries.
Solution Approach 2:
The system performs preliminary action by continuously pre-processing and indexing new content items as they are published, so that when users have interest queries, the latest information is already ready for immediate delivery. The future event detection model continuously scans for new content, identifies potential future events, and prepares them for notification, eliminating the need for users to repeatedly search for updated information.
Data Source
AI summary
One or more methods and/or techniques for providing a personalized future event notification to a user are provided herein. A content item (e.g., a news article, a social network post, etc.) may be evaluated utilizing a future event detection model to identify a future event (e.g., a festival). The future event detection model may have been trained to identify future events based upon part of speech analysis and entity recognition analysis of text within content items. In an example, the future event detection model may be used to identify locational features, temporal features, and/or entities from the content item. A user having a user interest in the future event above an interest threshold may be identified based upon user identifying information (e.g., a social network profile) being indicative of user interest in the future event. A personalized future event notification of the future event may be provided to the user.