Conversational Interface Personalization via Interaction-Based Interest Records
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Solution Overview
Problem
Existing conversational user interfaces struggle to tailor responses and digital components to a user's interests without requiring explicit user input, leading to inefficient and irrelevant interactions.
Innovation Solution
The system uses machine learning models to generate responses and display digital components within a conversational user interface, updating a user interest record based on user interactions and previous sessions to tailor content dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system displays generic digital components without personalization, then device complexity is reduced, but user experience and interaction efficiency deteriorate
Solution Approach 1:
The system automatically updates the user interest record by detecting user interactions with digital components and using machine learning models to infer interests, eliminating the need for explicit user input or manual configuration. The system serves itself by autonomously personalizing content based on observed behavior patterns.
Solution Approach 2:
The system implements a feedback loop where user interactions with displayed digital components are detected and fed back to update the user interest record. This continuous feedback mechanism allows the system to refine its understanding of user preferences and improve personalization over time.
2Adaptability or versatility
If the system collects and processes extensive user interaction data to personalize content, then user experience improves, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the essential features from user interaction data that are relevant to determining user interests. Rather than processing all raw interaction data, the machine learning models identify and extract key patterns and signals that indicate user preferences, reducing processing complexity while maintaining personalization effectiveness.
Solution Approach 2:
The system transforms raw user interaction data into standardized interest parameters and weights that can be efficiently stored and processed. By changing the parameter representation from detailed interaction logs to condensed interest profiles, the system reduces data processing requirements while preserving personalization capability.
3Measurement precision
If the system requests explicit user input for preferences, then personalization accuracy improves, but interaction efficiency and user convenience deteriorate
Solution Approach 1:
The system performs preliminary inference of user interests by analyzing interactions with initially displayed digital components before the user has a chance to provide explicit preferences. This preliminary action allows the system to establish a baseline personalization profile without requiring user input, improving interaction efficiency while maintaining reasonable personalization accuracy.
Solution Approach 2:
The system infers user preferences autonomously by detecting interactions with digital components, eliminating the need for users to explicitly state their preferences. The system serves itself by automatically understanding and adapting to user interests through observed behavior rather than direct communication.
4Measurement precision
If the system maintains detailed user interest records across multiple sessions, then personalization accuracy improves, but data storage requirements and privacy concerns increase
Solution Approach 1:
The system extracts and stores only the essential interest parameters and weights from detailed user interaction data. Rather than maintaining comprehensive logs of all user interactions, the system extracts key interest signals and stores them in a condensed format, reducing data storage requirements while preserving the ability to provide accurate personalization.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using artificial intelligence to display responses and digital components in a conversational user interface. A method includes initiating a user session with a conversational user interface of an artificial intelligence system. During the user session, the method includes receiving, by the artificial intelligence system, one or more prompts; displaying, in the conversational user interface, one or more digital components that each include content related to a corresponding item based at least in part on the one or more prompts, detecting, for each displayed digital component, one or more user interaction events; updating a user interest record, and displaying one or more additional digital components in the conversational user interface based at least in part on the user interest record.