Session Intent Classification via ML for Content Personalization
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Solution Overview
Problem
The increasing complexity of digital content and diverse user interactions make it difficult for content providers to accurately determine user intent and deliver personalized content, as existing search techniques are inefficient and fail to adapt to changing user behaviors and device types.
Innovation Solution
A system that collects user session logs to classify user intent by training a classification system using clustering algorithms and TF-IDF weighting, allowing for real-time classification of user sessions and personalized content delivery based on user actions, device type, and other factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword searching techniques are used, then search functionality is provided, but search efficiency and content discovery accuracy deteriorate due to the ever-expanding amount of digital content
Solution Approach 1:
The patent replaces traditional keyword-based mechanical search systems with a machine learning classification system that automatically analyzes user session logs, device types, and interaction patterns to determine user intent and recommend content, thereby improving both search efficiency and discovery accuracy without manual keyword matching
Solution Approach 2:
The system enables self-service by automatically training classification models on collected session data, continuously improving content recommendation accuracy without requiring manual intervention to update search algorithms or content indexes, allowing the system to adapt autonomously to changing user behaviors
2Adaptability or versatility
If content providers attempt to determine user intent for personalized content delivery, then content personalization is achieved, but system complexity increases due to diverse user interactions, social networks, and device types
Solution Approach 1:
The patent implements a universal classification system that handles multiple user interactions, social network contexts, and diverse device types through a single unified machine learning model, enabling content personalization across all scenarios without requiring separate complex systems for each interaction type
Solution Approach 2:
The system manages complexity by dynamically adjusting classification parameters and features based on the specific context (user session type, device category, social network presence), allowing the same base system to adapt to varying complexity requirements without being overwhelmed by any single scenario
3Adaptability or versatility
If existing search systems are used, then basic search functionality is provided, but they fail to adapt to changing user behaviors and device types
Solution Approach 1:
The patent implements continuous feedback loops where user session logs are collected, analyzed, and used to retrain classification models, allowing the system to automatically adapt to changing user behaviors and maintain high content delivery accuracy through iterative improvement based on actual user interactions
Solution Approach 2:
The system transitions from static keyword-based search to dynamic machine learning classification that continuously evolves with user behavior changes, allowing content delivery accuracy to be maintained or improved over time as the model adapts to new interaction patterns and device types
Data Source
AI summary
Described are systems and methods for determining session intent of a user. Different users can use a network-based application in many different ways based on, for example, the user's purpose for using the application, the device on which the user is executing the application, the user themselves, date, time, location, etc. Through the collection of user activities during a user session, the intent of a user session can be determined. Once determined, content provided through the application can be further personalized to correspond to the determined session intent.