Affinity Measure Computation for Mobile Content Personalization
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Solution Overview
Problem
Current mobile devices lack effective methods to determine user affinity and relevance for personalized content and interaction suggestions, leading to suboptimal user engagement and experience in social networking systems.
Innovation Solution
A social-networking system that utilizes a predictor module and affinity module to compute measures of affinity based on user actions and interactions, combining predictor functions to provide personalized content and interaction suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile devices store personal information and provide Internet-connected applications, then user engagement and interaction capabilities are enhanced, but the ability to determine user affinity and provide personalized content is insufficient
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and analyzing user actions, device usage patterns, and interaction data to pre-compute affinity measures before they are needed for personalization. This includes tracking app usage, communication patterns, and content interactions to build user profiles in advance, enabling the system to provide personalized content suggestions without losing affinity information.
Solution Approach 2:
The patent introduces an intermediary affinity determination system that acts as a mediator between raw user data and personalized content delivery. This intermediary layer collects diverse data sources (device usage, communication patterns, content interactions), processes them through analysis modules, and generates affinity measures that bridge the gap between user behavior and personalized content recommendation.
2Measurement precision
If the system monitors user actions and interactions to determine affinity, then personalization accuracy is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The affinity determination system is segmented into multiple independent modules: data collection modules that gather specific types of user actions, analysis modules that process different data types, and computation modules that calculate affinity measures. This segmentation allows each module to specialize in specific tasks, improving measurement precision while managing overall system complexity through modular architecture.
Solution Approach 2:
The system implements universal data collection and processing mechanisms that handle multiple types of user actions and interactions through a unified framework. The affinity determination apparatus uses multi-functional analysis modules that can process various data sources (communications, content interactions, device usage) using the same core algorithms, reducing complexity while maintaining precision.
Data Source
AI summary
In one embodiment, a method includes sending a request for a measure of affinity associated with a first user for a particular content associated with a second user, where the measure of affinity predicts a level of interest the first user has for the particular content; sending weighting information for computing the measure of affinity, where the weighting information includes information specifying a first weight to be attributed to a first predictor function that is based on the second user and a second weight to be attributed to a second predictor function that is based on concepts associated with the particular content; receiving the measure of affinity; and sending, to the first user, the particular content, based on the received measure of affinity.