Home Shopping Content Segmentation Using Viewer Interest Index
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Solution Overview
Problem
Conventional home shopping broadcast systems lack the ability to provide personalized content to specific viewer groups and are inefficient in scheduling desired products, relying on broadcast schedules that do not account for individual viewer interests.
Innovation Solution
A personalized home shopping broadcast system that dynamically selects and provides content based on viewer histories, using product classification groups determined by an interest index calculated from bounce rates, allowing for tailored content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional broadcast scheduling is used to transmit home shopping contents to all viewers, then the system can provide content to a large audience, but it cannot provide personalized content to specific viewer groups
Solution Approach 1:
The patent segments the homogeneous broadcast audience into distinct viewer groups based on viewing histories and interests. By dividing the audience into segments with similar preferences, the system can deliver personalized home shopping contents to each group while maintaining efficient broadcast distribution. This resolves the contradiction by enabling personalization (improving adaptability) through audience segmentation without requiring individual content delivery to each viewer (avoiding excessive system complexity).
Solution Approach 2:
The patent changes the parameter of content delivery from uniform broadcast to segmented delivery based on viewer interest parameters. By analyzing viewing histories and determining viewer groups with similar interests, the system dynamically adjusts which contents are broadcast to which groups. This parameter change enables personalization while maintaining broadcast efficiency, resolving the technical contradiction between adaptability and system complexity.
2Loss of time
If predetermined home shopping contents are transmitted according to broadcast schedules, then the broadcasting system operates efficiently, but viewers must wait for desired products to be scheduled
Solution Approach 1:
The patent performs preliminary analysis of viewer viewing histories and interests before content delivery. By pre-determining viewer groups and their preferred product categories in advance, the system can quickly select and broadcast relevant contents without requiring viewers to wait through scheduled programming. This preliminary action reduces viewer waiting time while maintaining broadcasting efficiency through automated group-based content selection.
Solution Approach 2:
The patent implements a feedback mechanism by analyzing viewer viewing histories and behaviors to determine interest levels in different product categories. This feedback information is used to dynamically adjust content selection and broadcasting schedules, ensuring that viewers receive relevant contents sooner. The feedback loop enables the system to reduce waiting time by broadcasting desired products when viewer interest is detected, without significantly compromising overall broadcasting efficiency.
3Reliability
If home shopping contents are customized for specific viewer groups, then viewer engagement increases, but the system requires complex analysis of viewing histories
Solution Approach 1:
The patent applies partial analysis by focusing on key viewing history parameters that are most indicative of viewer interests, such as frequently viewed product categories and viewing duration. Rather than analyzing every possible parameter, the system selectively processes the most relevant data points to determine viewer groups. This partial action approach maintains high content relevance (reliability) while reducing analysis complexity to manageable levels.
Solution Approach 2:
The patent enables the system to automatically analyze viewing histories and determine viewer groups without requiring manual intervention or complex external processing. The broadcast server performs self-service analysis by processing viewing history data and generating viewer group classifications autonomously. This self-service capability ensures high content relevance through automated personalization while keeping the system architecture relatively simple by avoiding the need for separate complex analysis systems.
Data Source
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AI summary
It is possible to dynamically select and provide home shopping content suitable for a specific viewer by using the viewing history of viewers of home shopping channels according to the present invention. In particular, a goods classification group is designated for each receiver by using an interest index calculated on the basis of a bounce rate defined according to the present invention, and the home shopping content can be dynamically selected and provided by using the goods classification group. As such, it is possible to provide home shopping content which more viewers are interested in.