Content Recommendation Agents for Cold-Start Personalization
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Solution Overview
Problem
Existing agent systems struggle to provide personalized advice to individual users or small community units like families due to the time required for agents to learn user preferences and optimize, leading to a 'cold start' issue when new users initiate the service.
Innovation Solution
An information processing system that stores personalized agents for users in a database and selects a base agent candidate based on the attributes and features of new users, utilizing a control unit to match and optimize the agent service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized sharing agent based on big data learning is provided, then it is possible to always interact with the latest agent even when a new user is added, but it is generally difficult to present advice personalized to an individual or a small community unit such as a family
Solution Approach 1:
The patent segments the agent system into multiple independent agents, each optimized for specific users or user groups. Instead of a single centralized agent, the system creates and maintains separate agents for different users, allowing each agent to be personalized to its target user while remaining part of the overall system architecture.
Solution Approach 2:
The patent applies local quality by making each agent have unique characteristics tailored to its specific user or user group. Each agent is optimized with local knowledge about its target user's preferences, behavior patterns, and requirements, allowing for personalized advice while maintaining system-wide coherence through the cloud-based platform.
2Ease of operation
If the agent learns user preferences individually, then personalized advice can be provided, but it takes some operations and time from initial state to optimization
Solution Approach 1:
The patent implements preliminary action by pre-optimizing agents for users before they actually need the service. Users can sign up in advance and have their agents trained and optimized in advance, so when the service is activated, the agent is already ready to provide personalized advice immediately without requiring initial learning period.
Solution Approach 2:
The patent uses copying by creating agent instances that can be replicated and distributed. When a new user signs up, the system can copy and adapt existing optimized agents to the new user, rather than training a completely new agent from scratch, significantly reducing the time required for initial optimization.
3Productivity
If a common agent optimized for all users grows on the cloud, then centralized sharing is enabled, but personalized advice to individuals or small communities cannot be effectively provided
Solution Approach 1:
The patent segments the monolithic common agent into multiple specialized agents, each serving specific users or user groups. This segmentation allows the system to maintain scalability through the cloud-based platform while enabling individual customization through specialized agents that understand their specific users' needs.
Solution Approach 2:
The patent applies dynamics by making the agent system flexible and adaptable. The cloud-based platform dynamically creates, manages, and optimizes agents based on real-time user data and preferences. Agents can be created, modified, and optimized dynamically as user needs change, allowing the system to scale while maintaining high levels of personalization.
Data Source
AI summary
An information processing system (1) that provide an agent service to users includes: a database that stores a plurality of agents optimized for respective users when the agent service is used; and a control unit (100) that selects a base agent candidate from the plurality of agents stored in the database according to an attribute or feature of a new user who newly uses the agent service.