Passive Profile System for Recommendation Engine Engagement
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Solution Overview
Problem
Existing recommendation engines struggle to provide relevant information to users who do not actively engage with the system, leading to disengagement due to insufficient user profiles and statistics, especially in smaller target audiences.
Innovation Solution
A passive profile system that analyzes and tracks user data to provide recommendations without requiring user action, using a combination of explicit, activity, and passive interaction profiles, along with filtering and clustering algorithms to deliver highly relevant items, peers, and services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the recommendation engine relies on explicit profiles and user actions to generate recommendations, then the system can provide personalized recommendations for highly engaged users, but it fails to provide relevant recommendations for least engaged users who do not enter or maintain information or take frequent actions
Solution Approach 1:
The system performs preliminary actions by proactively gathering data from multiple external sources (social media platforms, professional networks, industry databases) to create comprehensive passive profiles before users need recommendations. This advance data collection and profile construction enables the system to generate relevant recommendations for least engaged users without requiring them to first interact with the system
Solution Approach 2:
The patent introduces passive profiles as an intermediary representation that bridges the gap between external data sources and the recommendation engine. These passive profiles serve as mediators that translate diverse external data into a standardized format that the recommendation system can process, enabling recommendations for users who don't directly interact with the system
2Measurement precision
If the recommendation engine requires users to actively enter information or take actions to maintain engagement, then it can build accurate user profiles, but it creates a barrier that causes users to disengage
Solution Approach 1:
The system implements self-service by automatically collecting and processing user data from external sources without requiring user intervention. The passive profile generation and recommendation delivery occur autonomously, with the system serving itself by gathering data from social media, professional networks, and other external platforms to maintain accurate profiles and provide recommendations
Solution Approach 2:
The system performs preliminary data collection and profile construction before users need recommendations, gathering information from multiple external sources in advance. This advance preparation eliminates the need for users to actively enter information or take actions to maintain their profiles, thereby reducing engagement effort while maintaining profile accuracy
3Adaptability or versatility
If the recommendation engine provides a broad spectrum of marginally relevant choices to least engaged users, then it attempts to cover all possible user interests, but it becomes overwhelming and fails to break the cycle of disengagement
Solution Approach 1:
The patent applies local quality by tailoring the recommendation content and format to the specific characteristics of least engaged users. Rather than providing a uniform broad spectrum of recommendations to all users, the system adjusts the depth, breadth, and presentation of recommendations based on user engagement levels and inferred interests from passive profiles, delivering appropriately targeted content that is neither overwhelming nor too narrow
Solution Approach 2:
The system performs preliminary analysis of user data from external sources to infer interests and preferences before generating recommendations. This advance analysis enables the system to pre-filter and prioritize recommendations that are most likely to be relevant to least engaged users, reducing the overall volume of recommendations while maintaining high relevance and avoiding overwhelming users with marginally relevant choices
Data Source
AI summary
Computerized methods and systems for improving engagement with a recommendation engine that recommends items, peers, and services are provided. Stored data of a plurality of users is electronically accessed and analyzed. A respective passive profile is determined for each of the users based on the analyzing of the stored data. The respective passive profiles are then stored for use by the recommendation engine. The recommendation engine can then provide recommendations for at least one of items, peers, and services to a respective user based on at least the respective passive profile. The recommendations may be further based on at least one of an explicit profile comprising information provided by the respective user and an activity profile based on tracked activity of the respective user with regard to prior recommended items.