Server Apparatus Predictive Model for Multi-User Recommendation
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Solution Overview
Problem
Conventional recommendation techniques face challenges in providing personalized content to multiple users sharing a server apparatus, as they rely on static or incomplete user information, making it difficult to accurately predict user preferences and recommend appropriate content.
Innovation Solution
A server apparatus that uses a predictive model, based on viewing log data and user information, to predict user preferences and provide tailored content to each user, incorporating technologies like recurrent neural networks and convolutional neural networks to refine user profiling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation techniques use static or fragmentary user information, then the system is simple to operate, but the recommendation accuracy deteriorates because the amount of user information is very small or unreliable
Solution Approach 1:
The system performs preliminary user profiling by analyzing viewing log data before providing recommendations. The server apparatus collects and processes viewing history data to create user profiles in advance, enabling accurate recommendations without requiring users to directly provide extensive information at the time of recommendation requests
Solution Approach 2:
The patent introduces viewing log data as an intermediary element that bridges the gap between limited direct user information and accurate recommendation needs. By analyzing indirect behavioral data (viewing logs) rather than relying solely on direct user-provided information, the system achieves high recommendation accuracy while maintaining operational simplicity
2Device complexity
If the same server apparatus is used by a plurality of users, then the device complexity is reduced, but it becomes difficult to recommend appropriate content for each user due to inability to distinguish user preferences
Solution Approach 1:
The system segments user information by creating distinct user profiles within the shared server apparatus. Each user's viewing log data is processed separately to generate individualized profiles, allowing the single server to provide personalized recommendations to multiple users without requiring separate server instances for each user
Solution Approach 2:
The patent applies local quality by tailoring the recommendation output to each specific user based on their individual profile characteristics. The server apparatus maintains a unified structure but generates user-specific recommendations by applying locally adapted processing to each user's viewing behavior patterns
3Measurement precision
If the server apparatus collects extensive user information to improve recommendation accuracy, then the recommendation quality improves, but the cost and complexity of information collection increases significantly
Solution Approach 1:
The system implements self-service by automatically collecting and processing viewing log data without requiring active user participation in information provision. Users simply use the broadcast receiving apparatus normally, and the server apparatus autonomously gathers viewing behavior data, processes it into profiles, and generates recommendations without requiring users to complete surveys or provide explicit information
Data Source
AI summary
Disclosed is a server apparatus. The server apparatus comprises: a communication unit for receiving, from an external server, multiple first viewing log data for each of a plurality of first users using a first broadcast receiving apparatus and multiple user information of each of the plurality of first users; and a processor for updating a predictive model for predicting, from the viewing log data, the number of users using the broadcast receiving apparatus and user information of each of the users, on the basis of the received multiple first viewing log data and the received multiple user information, wherein, when second viewing log data is received from a second broadcast receiving apparatus through the communication unit, the processor predicts, using the updated predictive model, the number of users using the second broadcast receiving apparatus and user information of each of the users.