Universal Interest Space for User Engagement Measurement
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Solution Overview
Problem
Conventional content recommendation systems fail to accurately capture users' long-term interests and often provide fragmented representations of user preferences due to reliance on short-term interactions and isolated application settings, leading to ineffective personalization and limited discovery of new interests.
Innovation Solution
A system that generates a universal interest space using public concept archives like Wikipedia, allowing for the creation of high-dimensional vectors to represent users and content, enabling a more coherent understanding of user interests and preferences, and incorporating various user activities across different devices and settings to estimate engagement scores for personalized content recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional content recommendation systems rely on short-term interactions and isolated application settings, then implementation simplicity is maintained, but user interest representation accuracy deteriorates
Solution Approach 1:
The patent merges user interactions from multiple application settings and devices into a unified user profile. By combining click data, skip data, and engagement metrics across different contexts (news feeds, search results, video streams), the system creates a comprehensive representation of user interests that transcends isolated application boundaries, thereby improving measurement precision without proportionally increasing complexity
Solution Approach 2:
The patent implements a universal interest space that functions across multiple applications and devices. The user profile system serves multiple purposes: it personalizes content recommendations, measures engagement accuracy, and adapts to different interaction contexts. This multi-functional approach allows the same core mechanism to improve user interest representation across diverse settings without requiring separate systems for each application
2Measurement precision
If systems use traditional CTR metrics for user profiling, then implementation simplicity is maintained, but user engagement measurement accuracy deteriorates
Solution Approach 1:
The patent changes the parameters used for user profiling from simple CTR metrics to a multi-dimensional engagement model. It incorporates click frequency, skip frequency, time spent on content, and interaction patterns across different content types and positions. By transforming the measurement parameters from single-metric to multi-metric, the system achieves superior engagement measurement accuracy while managing complexity through systematic data aggregation
Solution Approach 2:
The patent introduces an intermediary engagement scoring mechanism that translates raw interaction data (clicks, skips, time spent) into meaningful engagement metrics. This intermediary layer processes and synthesizes multiple data sources before generating user profiles, thereby improving measurement accuracy while shielding the core recommendation system from the complexity of raw data processing
3Adaptability or versatility
If systems rely on passive past behavior data, then data collection simplicity is maintained, but user interest discovery capability deteriorates
Solution Approach 1:
The patent implements preliminary probing mechanisms that actively test user interests before full commitment. By introducing diverse content types, positions, and formats into user feeds and measuring reactions (clicks, skips, engagement time), the system proactively discovers potential interests before they manifest in explicit user behavior. This preliminary exploration enables new interest discovery while maintaining manageable complexity through structured experimentation
Data Source
AI summary
Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.