Webcast Recommendation Engine Using Social Affinity Graphs
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Solution Overview
Problem
Current methods for inviting attendees to webcasts or online virtual events rely on mass email campaigns and broad assumptions, failing to consider the entire history of individuals' interactions, leading to untargeted and uninteresting content recommendations.
Innovation Solution
A recommendation system that utilizes historical interest data of current attendees and selections made by others with shared interests to provide personalized recommendations for webcasts or virtual environments, creating a social graph to identify top co-viewers and filter recommendations based on compatibility scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mass email campaigns and broad assumptions are used to invite attendees to webcasts, then audience generation can be achieved through simple tactics, but the recommendations become untargeted and uninteresting
Solution Approach 1:
The patent segments the audience into individual user profiles with distinct interaction histories, interests, and behaviors. Instead of treating all attendees as a homogeneous group, the system divides them into personalized segments based on their unique engagement patterns with webcasts and virtual events, enabling targeted recommendations for each user.
Solution Approach 2:
The patent adds a new dimension of personalization by incorporating individual user interaction histories and affinity scores with other users. This transforms the recommendation system from a two-dimensional approach (current webcast + generic related content) to a multi-dimensional approach that includes historical behavior, user preferences, and social affinity factors.
2Speed
If current webcast context only is used for recommendations, then recommendations can be generated quickly, but the entire history of individuals' interactions is not considered
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing users' interaction histories, preferences, and affinity scores in databases before recommendations are needed. User profiles are built in advance with comprehensive data about their webcast attendance, virtual environment participation, and interactions with other users, so that when recommendations are generated, this pre-processed information can be quickly retrieved and applied.
Solution Approach 2:
The system implements feedback mechanisms that continuously update user profiles based on their interactions with recommended content and other webcasts. User affinity scores are dynamically adjusted based on their viewing patterns and interactions, creating a feedback loop that refines recommendations over time while maintaining speed through efficient data structures and algorithms.
3Device complexity
If broad assumptions are made about the entire audience, then recommendations can be generated with minimal data processing, but overly general and uninteresting content is delivered
Solution Approach 1:
The patent applies local quality by providing different recommendation strategies and content selections tailored to each user's specific characteristics, preferences, and interaction history. Instead of applying a uniform recommendation approach to all users, the system adjusts the recommendation algorithm and content selection based on individual user profiles, ensuring that each user receives locally optimized recommendations suited to their specific interests.
Data Source
AI summary
A webcast and virtual environment content recommendation engine and method provide webcast and/or virtual environment content recommendation using user history and affinity with other individuals to predict interesting current future webcasts and online virtual environments and content.