Content Recommendation System Using Viewer Feedback
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Solution Overview
Problem
Users face difficulty in selecting desired content from the vast array of options available on content reproducing devices, necessitating a method for providers to collect and utilize user information to offer personalized content recommendations.
Innovation Solution
A system where a content reproduction device communicates with a server to receive recommended content information based on viewer ratings, user data, and viewing habits, allowing for personalized content selection and output on the device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content reproducing devices provide numerous content options, then content variety increases, but user selection difficulty increases
Solution Approach 1:
The system collects viewing information from multiple content reproduction devices and uses this feedback to generate viewer ratings. These ratings are then used to recommend content that other users have viewed, creating a feedback loop that helps users make selection decisions without manually browsing through all available content options.
Solution Approach 2:
A server acts as an intermediary between content reproduction devices and users. The server collects viewing information, generates viewer ratings, and provides recommended content information to devices. This intermediary processing simplifies the user experience by presenting curated recommendations rather than requiring direct user interaction with the full content library.
2Measurement precision
If service providers collect user information, then content recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The server performs multiple functions: collecting viewing information from devices, generating viewer ratings based on this information, and providing recommended content. By consolidating these functions in a single multi-functional server, the system achieves accurate recommendations without distributing complexity across multiple specialized components.
Solution Approach 2:
The system transforms raw viewing information into viewer ratings through parameter transformation. By changing the form of data from individual viewing records to aggregated rating metrics, the system improves recommendation accuracy while simplifying the complexity of processing individual user data points.
3Ease of operation
If viewer rating information is used for content selection, then content selection ease improves, but information processing requirements increase
Solution Approach 1:
The system extracts only the essential viewing information needed to generate viewer ratings from the vast amount of data collected from multiple devices. By taking out and processing only the relevant parameters (viewing counts, user demographics), the system enables easy content selection without requiring processing of all available information.
Data Source
AI summary
A content reproduction device includes a processor configured to request the server via the communicator for recommended content information in response to setting a content recommendation mode, receive the recommended content information from the server via the communicator, determine content based on the recommended content information and information regarding a user of the content reproduction device, and determine whether the user has reproduction authorization regarding the determined content. When determining that the user has no reproduction authorization regarding the determined content, the processor is configured to control the display to display a user interface for purchasing the reproduction authorization regarding the determined content, receive the determined content via the communicator from the server in response to receiving a user input for purchasing the reproduction authorization based on the user interface, and control the display to display the received content.