Time-Based Viewing Clustering for Personalized Channel Lists
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Solution Overview
Problem
Users face difficulty in selecting desired content from a large variety of content options provided by display devices, necessitating a method to recommend channels personalized to their viewing habits.
Innovation Solution
A server obtains use history information, clusters user characteristics by time and overall use characteristics to generate a recommended channel list, providing personalized recommendations based on grouped user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content diversification is increased to provide various types of content, then content variety is improved, but user selection difficulty increases
Solution Approach 1:
The system automatically analyzes user viewing history and behavior patterns to generate personalized channel recommendations without requiring manual user input or search operations. The server autonomously processes viewing history data, clusters users based on similarities, and provides tailored channel lists, enabling the system to serve itself in the recommendation generation process.
Solution Approach 2:
The system utilizes user viewing history as feedback data to continuously refine and personalize channel recommendations. By analyzing past viewing behaviors and updating user profiles based on accumulated data, the system adapts its recommendations to match evolving user preferences, creating a closed-loop feedback mechanism that improves recommendation accuracy over time.
2Measurement precision
If personalized recommendations are generated for each user, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The system merges users with similar viewing characteristics into clusters, treating them as a group for recommendation purposes. By combining multiple individual user profiles into consolidated cluster profiles, the system reduces the computational burden of processing each user separately while maintaining personalized recommendation capabilities through cluster-based targeting.
Solution Approach 2:
The server implements a multi-functional system that performs data collection, analysis, clustering, and recommendation generation within a single platform. The system serves multiple purposes: storing viewing history, analyzing user behavior, creating user clusters, and generating personalized recommendations, thereby reducing overall system complexity through functional integration.
3Measurement precision
If viewing history data is collected and analyzed, then recommendation personalization is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary clustering of users based on their viewing characteristics in advance, creating pre-grouped user clusters before recommendation requests are made. By pre-processing and organizing user data into clusters, the system reduces the computational time required during actual recommendation generation, as the clustering structure is already established and ready for efficient query processing.
Data Source
AI summary
A server obtains use history information including time information of a plurality of display devices, obtains, based on the use history information, use characteristics by the time information and overall use characteristics for each of the plurality of users corresponding to a plurality of display devices, groups the use characteristics by the time information for a plurality of users into M groups by clustering, and the overall use characteristics for a plurality of users into N groups by clustering, obtains a recommended channel list including at least one of a first preferred channel corresponding to a first group including use characteristics by the time information of a first user among the M groups or a second preferred channel corresponding to a second group including overall use characteristics of the first user among the N groups, and provides the recommended channel list to a display device corresponding to the first user.