Contextual Recommendation Engine for Personalized User-Event Matching
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Solution Overview
Problem
Social networking systems fail to provide contextually relevant recommendations and advertisements, overwhelming users with non-relevant information and limiting the effectiveness of real-world interactions due to the reliance on non-meaningful data for user interests and interactions.
Innovation Solution
A recommendation engine that connects users with relevant events and other users based on their interests, location, and behavior, using algorithms to provide personalized recommendations through various channels, including augmented reality, and contextualizing data from external systems to promote real-world interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If social networking systems deliver information based on user grouping by common attributes, then information delivery can be customized and targeted to groups, but users are overwhelmed with large amounts of non-relevant information
Solution Approach 1:
The patent applies local quality by transitioning from group-level information delivery to individual-level customization. Each user receives information tailored to their specific profile attributes, interests, and behavior patterns rather than receiving the same group-targeted content. This is achieved through machine learning models that analyze individual user data to personalize information delivery, thereby maintaining adaptability while reducing the quantity of non-relevant information each user receives.
Solution Approach 2:
The patent employs parameter changes by using machine learning models to dynamically adjust information delivery parameters based on individual user characteristics. The system analyzes multiple user parameters (profile attributes, interests, behavior patterns) and uses these to optimize information delivery relevance. This allows the system to maintain high adaptability while filtering out non-relevant information, thus reducing the overall quantity of information each user receives.
2Adaptability or versatility
If advertisers use members' affinities as targeting criteria, then advertisements can be targeted to specific groups, but members are inundated with advertisements unrelated to their current context
Solution Approach 1:
The patent applies preliminary action by pre-analyzing user profiles, interests, and behavior patterns using machine learning models before delivering advertisements. The system prepares personalized advertising profiles in advance that predict which advertisements will be relevant to each user based on their current context and historical behavior. This preliminary analysis enables advertisers to target individuals with highly relevant ads rather than broad group targeting, reducing ad blindness while maintaining targeting capability.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors user interactions with advertisements and adjusts future advertising delivery based on this feedback. Machine learning models analyze user responses to refine advertising relevance over time, ensuring that targeted advertisements remain contextually appropriate. This feedback loop reduces ad blindness by learning from user behavior and improving targeting accuracy while preserving the adaptability of advertising delivery.
3Loss of information
If social networks rely on page likes and followers to indicate user interests, then user interests can be tracked, but the data is largely non-useful and non-meaningful for real-world interactions
Solution Approach 1:
The patent replaces the mechanical system of explicit user actions (page likes, followers) with a machine learning-based inference system that analyzes multiple behavioral signals. Instead of relying solely on deliberate user actions, the system uses algorithms to infer genuine interests from patterns in user behavior, profile attributes, and interaction history. This substitution transforms superficial data into meaningful insights about user interests that are relevant for real-world interactions, reducing information loss while improving data reliability.
Solution Approach 2:
The patent creates composite interest profiles by combining multiple data sources including profile attributes, behavior patterns, interaction history, and inferred interests from machine learning models. Rather than relying on a single indicator like page likes, the system synthesizes diverse information sources to create a comprehensive and reliable representation of user interests. This composite approach ensures that the tracked information is both complete (reducing loss) and meaningful (improving reliability) for real-world interactions.
Data Source
AI summary
A method for comprehensive user/event matching or recommendations is described. The method includes a network environment which receives one or more pieces of user data, event data, or social data from users or third party data sources, determining the relevance of the data for users, and displaying the identified data to user in the form of recommendations.