Day-Specific Content Pages Using Personalized Recommendation Thumbnails
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Solution Overview
Problem
Existing content providing systems fail to effectively customize and intuitively recommend contents based on the day of the week they are serialized, leading to suboptimal user engagement and continuous consumption.
Innovation Solution
A method and system that identifies user accounts and provides customized recommended contents on a page distinguished by the day of the week, using thumbnails with recommendation indicators, sorted by predetermined criteria, to enhance user interaction and content consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If contents are serialized on specific days of the week to ensure continuous consumption, then user engagement is improved, but user convenience deteriorates because users must remember to visit contents on specific days
Solution Approach 1:
The system provides feedback to users by displaying recommended contents that are expected to be preferred by each user, based on their viewing history and preferences. This feedback mechanism helps users discover relevant contents without needing to remember specific serialization days, thereby maintaining continuous consumption while improving user convenience.
Solution Approach 2:
The content providing service performs self-service by automatically recommending contents based on user preferences and viewing history. The system autonomously determines which contents to recommend and presents them to users, eliminating the need for users to actively remember or search for specific serialization days, thus improving ease of operation while maintaining continuous consumption.
2Productivity
If customized recommended contents are provided for each user based on their preferences, then user engagement is improved, but device complexity increases due to need for user profile tracking and recommendation algorithms
Solution Approach 1:
The system performs self-service by automatically tracking user viewing history and generating personalized recommendations without requiring manual input or complex user configuration. The recommendation algorithm autonomously processes user data and presents customized contents, improving user engagement while keeping the interface simple and avoiding excessive complexity in the user-facing system.
Solution Approach 2:
The patent replaces complex manual content selection mechanisms with automated recommendation algorithms that process user viewing history and preferences. This substitution reduces the need for users to manually search or select contents, improving engagement while managing system complexity through automated intelligence rather than manual complexity.
3Ease of operation
If contents are organized by day of the week with thumbnails listed, then content organization is improved, but information completeness deteriorates because not all content details are immediately visible
Solution Approach 1:
The system segments content information into two levels: thumbnail-level summaries displayed on the main page for quick browsing and organization by day of the week, and detailed content information available upon user interaction (e.g., clicking on a thumbnail). This segmentation maintains excellent content organization and accessibility while preserving information completeness, as all details are available but not immediately displayed, reducing clutter while maintaining organization.
Data Source
AI summary
A method of providing contents according to the present invention may include receiving, from a user terminal, a request to output a specific page corresponding to a specific day of the week among a plurality of pages distinguished on the basis of the day of the week; identifying a specific user account logged in to the user terminal, and identifying a recommended content that is recommended for the specific user account among a plurality of contents provided on the specific day of the week; and providing, to the user terminal, at least some of the plurality of contents being serialized on that specific day of the week and the specific page in which a plurality of thumbnails corresponding to each of the plurality of contents are listed according to predetermined sorting criteria. A specific thumbnail corresponding to the recommended content among the plurality of thumbnails may include a recommendation indicator.