Personalized Self-Help Experience via Predictive Tagging
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Solution Overview
Problem
Existing online applications face challenges in providing personalized and relevant self-help experiences for users without human assistance, as they encounter complex and unique issues, necessitating an improved approach to suggest relevant questions and answers.
Innovation Solution
A predictive model is trained using machine learning to learn the relationship between user features and tags, predicting tag probabilities from incoming user data, and calculating similarity metrics between high-dimensional vectors to return the most relevant question titles to users, dynamically adapting to user behavior and clickstream activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional static knowledge base is used, then the system structure is simple, but the personalization capability is poor
Solution Approach 1:
The patent implements dynamic personalization by transitioning from a static knowledge base to a dynamic system that adapts to user behavior in real-time. The system continuously learns from user interactions, clickstream data, and feedback to update user profiles and personalize content delivery, making the knowledge base adaptive rather than fixed.
Solution Approach 2:
The patent segments users into distinct groups based on their behavior patterns, preferences, and characteristics. By dividing the user base into segments, the system can deliver personalized content to each segment without requiring complete customization for every individual user, thus managing complexity while improving personalization capability.
2Productivity
If manual content curation is used, then content quality is high, but the scalability is poor
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically curates and personalizes content without requiring manual intervention. The machine learning models autonomously analyze user data, generate personalization strategies, and deliver relevant content, enabling the system to serve itself and scale efficiently without proportionally increasing human resources for content curation.
Solution Approach 2:
The patent replaces manual mechanical content curation processes with automated machine learning systems. Instead of human experts manually analyzing and delivering content, the system uses algorithms to automatically process user data, generate personalization recommendations, and deliver content at scale, dramatically improving productivity while reducing time investment.
3Ease of operation
If generic recommendations are provided, then the system complexity is low, but the user engagement is poor
Solution Approach 1:
The patent changes key parameters of the recommendation system by incorporating multiple dimensions of user data including behavior patterns, preferences, demographics, and contextual information. By adjusting and optimizing these parameters through machine learning, the system generates highly relevant personalized recommendations that significantly improve user engagement while maintaining operational simplicity through automated processes.
4Adaptability or versatility
If real-time personalization is implemented, then the adaptability is high, but the computational resources required increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and pre-segmenting user data before real-time personalization is needed. The system performs initial data cleaning, feature extraction, and user segmentation in advance, storing processed results for quick retrieval during real-time interactions. This approach enables real-time adaptability while significantly reducing computational resource consumption during actual personalization delivery.
Data Source
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AI summary
Method and apparatus for providing personalized self-help experience in online application. A predictive model is trained to learn a relationship between one or more user features and one or more tags using historical user feature data. High-dimensional vectors representing each of a plurality of questions are generated and stored in the lookup table. The trained predictive model outputs tags probabilities from the incoming user data, using the learned relationship. A user high-dimensional vector is formed based on the tags probabilities. Similarity metrics are calculated between the highdimensional vector for the respective question and the user high dimensional vector. One or more of the most relevant question titles are returned to a client device for presentation to a user.