Personalized Event Recommendation via Predicted Favorite Score
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Solution Overview
Problem
Existing systems lack an effective mechanism to recommend video events to users based on their personalized preferences, leading to reduced user engagement and revenue generation for event organizers.
Innovation Solution
A processor-based system that receives user and event features, determines a predicted favorite score for each event by analyzing user interactions and event characteristics, and displays events to users based on these scores, utilizing machine learning models to optimize event recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems display events without personalized recommendations, then the system complexity is low, but user engagement and revenue generation are reduced
Solution Approach 1:
The system automatically generates personalized event recommendations by analyzing user features and event features without requiring manual curation or complex user intervention. The machine learning model autonomously processes data to create tailored event lists for each user, enabling the system to serve itself in generating relevant content recommendations.
Solution Approach 2:
The system transforms raw user and event data into a predicted favorite score parameter, which serves as a ranking metric for event recommendations. By converting multiple input features (user preferences, event characteristics) into a single predictive parameter, the system simplifies the recommendation generation process while improving user engagement.
2Measurement precision
If the system analyzes multiple user and event features to generate predictions, then recommendation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system divides the recommendation task into separate processing stages: feature extraction from user and event data, feature transformation into model inputs, prediction generation, and result ranking. This segmentation allows each component to be optimized independently and enables parallel processing of features, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The system pre-processes and stores user features and event features in structured formats before generating predictions. By preparing and organizing the data in advance, the system reduces the computational burden during prediction generation, leading to faster processing times without sacrificing the comprehensive analysis needed for accurate recommendations.
Data Source
AI summary
One example system event recommendation includes a processor and at least one memory device. The memory device includes instructions that are executable by the processor to cause the processor to receive a plurality of user features associated with a user, receive a plurality of event features, each of said event features associated with one or more events, and determine a predicted favorite score personalized for each user for each of the one or more events, the predicted favorite score based at least in part on the plurality of user features and the plurality of event features. The memory device also includes instructions that cause the processor to display at least one of the one or more events to the user based at least in part on the personalized predicted favorite score associated with each of the one or more events.