Multimedia Content Pushing Using Real-Time User Conversion Signals
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Solution Overview
Problem
Existing multimedia content pushing methods lack relevance and accuracy, as they rely solely on historical user data without considering real-time interactions, leading to ineffective content recommendations.
Innovation Solution
A method utilizing historical and real-time operation information of multiple users to identify a target sample user whose type is converted from a first type to a second type, training a neural network model based on this data, and determining a push strategy to refine content recommendations using the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If historical operation information is used to determine pushed multimedia content, then content pushing can be performed, but the relevance and accuracy of content recommendation deteriorates
Solution Approach 1:
The patent transitions from static historical data analysis to dynamic real-time data processing. The system continuously collects real-time operation information from users and dynamically updates user profiles and content recommendations, enabling the system to adapt to changing user preferences and behaviors, thereby improving recommendation accuracy while maintaining pushing capability
Solution Approach 2:
The patent implements a feedback mechanism where real-time user operations are collected, analyzed, and used to refine future recommendations. The system monitors user interactions with pushed content and uses this feedback to adjust the recommendation algorithm, creating a continuous improvement loop that enhances accuracy over time
2Device complexity
If only historical data is analyzed, then processing complexity is reduced, but the pertinence of content delivery deteriorates
Solution Approach 1:
The patent segments the data processing task into distinct modules: historical data collection, real-time data collection, user profile management, and recommendation generation. This segmentation allows the system to handle complex real-time processing by breaking it down into manageable components, each processed independently and then integrated, thereby reducing overall system complexity while improving delivery pertinence
Solution Approach 2:
The patent performs preliminary actions by pre-processing historical data to create baseline user profiles and content catalogs before real-time processing begins. This preliminary setup reduces the computational burden during real-time operations, as the system only needs to update and refine existing profiles rather than create everything from scratch, thus managing complexity while maintaining high pertinence
Data Source
AI summary
A multimedia content pushing method and an apparatus, a computer device, and a storage medium are provided. The method includes: obtaining historical operation information of alternative users on a target multimedia content in a target historical time period and real-time operation information on the target multimedia content; determining a target sample user from the alternative users based on the historical operation information and the real-time operation information, and training a neural network model based on sample data composed of sample attribute information corresponding to the target sample user and multimedia attribute information of the target multimedia content to obtain a target neural network model, and in response to a target user of the first type triggering a preset push event, using the target neural network model to determine a push strategy, and pushing the target multimedia content based on the push strategy.