Machine Learning Event Recommendation Engine
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Solution Overview
Problem
Users operating remotely often struggle to stay informed about events within an organization, such as webinars and conference calls, as these events are typically advertised through email or direct messaging platforms, limiting their reach beyond established mailing lists, and they may not interact with administrators to be added to relevant groups.
Innovation Solution
A machine learning-based platform that recommends upcoming events by tagging them with keywords, allowing users to subscribe to specific tags and integrating with calendars to suggest events based on their interests and availability, using a reinforcement learning model to improve recommendation accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If events are advertised through email and direct messaging platforms, then information can be communicated to users, but the reach is limited to established mailing lists and groups
Solution Approach 1:
The patent introduces an intermediary system (recommendation engine with machine learning models) that bridges the gap between event organizers and users. This intermediary analyzes user profiles, event data, and interaction patterns to automatically match users with relevant events, expanding reach beyond traditional mailing lists without requiring direct user-administrator interaction
Solution Approach 2:
The system enables users to automatically receive relevant event recommendations without needing to manually subscribe to mailing lists or interact with administrators. The machine learning model continuously learns from user interactions and autonomously optimizes event matching, allowing users to self-serve their information needs
2Loss of information
If users must interact with administrators to be added to mailing lists, then they can receive event information, but remote users are less likely to do so
Solution Approach 1:
The system eliminates the need for users to manually interact with administrators by implementing automatic event recommendation and delivery. The machine learning model autonomously identifies relevant users and delivers event information based on analyzed user preferences and interaction patterns, making the process completely self-service oriented
Solution Approach 2:
The system performs preliminary analysis of user profiles, preferences, and interaction patterns before events are advertised. This advance preparation enables the system to proactively identify and reach relevant users without requiring them to take any action to join groups or express interest
3Measurement precision
If machine learning models are trained with more interaction data, then recommendation accuracy improves, but the system requires more user interactions to start
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user profile data, preferences, and existing interaction patterns before formal tracking begins. This head start allows the model to generate initial recommendations sooner while continuing to improve accuracy as more interaction data becomes available
Solution Approach 2:
The system implements continuous feedback loops where user interactions with recommendations (clicks, attendance, engagement) are immediately fed back into the machine learning model. This real-time feedback accelerates learning and improves recommendation accuracy more quickly than batch processing would allow
Data Source
AI summary
Disclosed herein are examples of systems and methods for recommending events using machine learning. A first recommendation can be generated based at least in part on at least one user parameter associated with a user. The first recommendation can comprise a first event. The first recommendation can be provided to a client device associated with the user, and a user response to the recommendation can be received from the client device. A second recommendation can be generated based at least in part on the user response and the at least one user parameter, wherein the second recommendation comprises a second event. The second recommendation can be provided to the client device.