Personalized Search via Dynamic User Profile Vector Correlation

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Solution Overview

Problem

Conventional search engines require users to explicitly input their interests, which can lead to inaccuracies if interests change, and they typically filter content based on pre-selected categories without considering the degree of interest.

Innovation Solution

A system that generates a content vector and a profile vector based on user behavior, correlating them to personalize content by identifying the most similar content vectors and weighting them according to user interest, allowing for dynamic adaptation and relevance ranking without explicit user input.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If users explicitly select preferred topics to personalize headlines and search results, then personalization accuracy is improved, but user operation complexity increases and the system cannot adapt when interests change without new input

Engineering Contradiction:

Improvepersonalization accuracyVSAvoiduser operation complexity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system automatically monitors user actions (clicks, views, searches) and dynamically updates interest profiles without requiring explicit user input. The algorithm self-adjusts personalization based on observed behavior patterns, eliminating the need for users to manually select topics while maintaining accurate personalization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously collects feedback from user interactions with content and uses this feedback to refine interest profiles in real-time. By analyzing click-through rates, time spent on pages, and search patterns, the system adapts to changing user interests automatically, resolving the contradiction between accuracy and operational simplicity.

Inventive Principle:

Principle #23Feedback

2Speed

If search engines use pre-selected categories to filter content, then processing speed is improved, but personalization accuracy deteriorates because the degree of user interest is not considered

Engineering Contradiction:

Improvecontent processing speedVSAvoidpersonalization accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The system transforms categorical filtering into a continuous interest scoring system. Instead of binary category matching, the algorithm calculates interest scores based on user behavior frequency and recency, allowing for nuanced personalization that maintains processing efficiency through algorithmic optimization while significantly improving personalization accuracy.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system adds a temporal dimension to content filtering by considering the recency and evolution of user interests. By incorporating time-weighted interest scores and analyzing trends in user behavior over time, the system achieves more accurate personalization without sacrificing processing speed through efficient temporal pattern recognition.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS7664746B2Personalized search and headlines

Publication Date: 2010.02.16 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

The claimed subject matter provides a system and/or a method that facilitates providing a personalized set of available content. An interface can receive a content vector associated with available content and a profile vector associated with a user preference. Additionally, a personalization component can generate a personalized set of available content by correlating the content vector and the profile vector.