ML Supervisor for Chat App Bot Content Relevance
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Solution Overview
Problem
The display of excessive or extraneous information by third-party applications in primary user applications, such as chat applications, can counterproductively affect user experience due to conflicting or irrelevant content recommendations.
Innovation Solution
A supervisor system utilizing a machine learning model to curate and customize the display of additional content generated by secondary applications based on contextual information and user feedback, selecting a subset of content items for display in the primary user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple third-party applications generate display recommendations to enhance user experience, then the functionality and information richness of the primary application is improved, but the user interface becomes cluttered with excessive or conflicting information
Solution Approach 1:
A supervisor application is introduced as an intermediary between the primary application and third-party applications. The supervisor receives display recommendations from multiple third-party applications, processes them through a machine learning model, and curates a subset for display in the primary application's user interface. This mediator filters and coordinates the information flow, preventing direct clutter while maintaining functionality.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with displayed content are monitored and fed back into the machine learning model. This feedback loop enables the model to continuously improve its recommendations by learning from user responses, ensuring that displayed information remains relevant and does not create confusion or clutter over time.
2Quantity of substance
If third-party applications provide additional information recommendations, then the information richness is improved, but the relevance and quality of displayed information deteriorates due to extraneous or conflicting content
Solution Approach 1:
The machine learning model utilizes feedback from user interactions to continuously refine its recommendations. User responses to displayed content are monitored and fed back into the model, enabling it to learn what information is actually useful to users and adjust future recommendations accordingly, thereby maintaining high relevance despite the large quantity of available information.
Solution Approach 2:
The system dynamically changes parameters of information selection and presentation based on contextual factors and user behavior patterns. The machine learning model adjusts the threshold for information relevance, the timing of recommendations, and the formatting of displayed content based on real-time analysis of user interactions and contextual data.
Data Source
AI summary
Disclosed is a system for managing content generated by bots for presentation to a user in association with a chat application. The system receives content items generated by bots monitoring a chat application for display to a user at a user interface (UI). The system provides input based on the received one or more content items and associated contextual information to a trained machine learning (ML) model, and receives, from the trained ML model, for each of the content items, at least one score value based on at least one predicted user response associated with potentially displaying the content item to the user at the UI. The system selects a subset of content items from the received content items based on the received score values and causes a display of this selected subset of content items in addition to a display of content generated by the chat application.