Event Recommendation System Using Message Parsing
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Solution Overview
Problem
Users spend significant time and computing resources tracking future events of interest, often missing notifications due to the inefficiency of manually searching across various platforms.
Innovation Solution
A system that utilizes a message history repository to parse messages for event attendance history, employing machine learning and entity recognition to extract event features, which are then used to query an event database for future events of interest, filtered by location and user travel plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually track events across multiple platforms, then they can find future events of interest, but they spend substantial time and computing resources doing so
Solution Approach 1:
The system automatically tracks and monitors events across multiple platforms without requiring user intervention. The event recommendation system autonomously parses messages, extracts event features, queries event databases, and generates recommendations, freeing users from manual tracking while maintaining reliable event information
Solution Approach 2:
The system introduces an intermediary layer between users and event information sources. Instead of users directly monitoring multiple platforms, the recommendation system acts as a mediator that collects, processes, and filters event information, then presents relevant recommendations to users
2Reliability
If users manually search for events, then they can identify future events, but they may miss notifications due to inefficiency
Solution Approach 1:
The system continuously monitors and updates event information automatically. By maintaining continuous engagement with event databases and message sources, the system ensures users are always informed of relevant events without requiring manual search operations
Solution Approach 2:
The system provides feedback to users through event recommendations based on their past attendance history. This feedback loop continuously improves by learning from user behavior patterns, making the system increasingly accurate in predicting and recommending relevant future events
3Measurement precision
If the system processes all user messages to extract event features, then event recommendation accuracy improves, but computing resources are consumed
Solution Approach 1:
The system extracts only the relevant event features from user messages using machine learning models. Instead of processing all message content equally, the system identifies and extracts specific features such as event names, dates, locations, and performer information, reducing computational overhead while maintaining accuracy
Solution Approach 2:
The system applies partial processing by focusing computational resources on the most relevant message segments and features. Through prioritization of message processing based on user behavior patterns and event relevance, the system achieves good accuracy without processing every detail of every message
4Reliability
If the system tracks all past events, then future event prediction improves, but data storage and processing complexity increase
Solution Approach 1:
The system extracts and stores only the essential event features from past attendance history rather than retaining complete message records. By storing condensed event information such as performer names, event types, dates, and locations, the system maintains prediction accuracy while significantly reducing data storage and processing requirements
Solution Approach 2:
The system segments event data into distinct features and categories (performer information, event details, temporal data, location data). This segmentation allows for more efficient processing and storage while enabling sophisticated pattern recognition for future event prediction
Data Source
AI summary
One or more computing devices, systems, and/or methods for future event recommendation are provided. For example, messages of a user account of a user are parsed to identify messages corresponding to events attended by the user. Event features are extracted from the messages, where an event feature is descriptive of an aspect of an event. An event database of future events is queried using the event features to identify future events having one or more of the event features. A description of the future events is constructed and provided to the user.