Content Recommendation Server for Meeting-Based Sharing
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Solution Overview
Problem
Users cannot efficiently share content during calls, and existing methods are slow in finding desired content, leading to a demand for a technique that recommends shareable content based on users' previous activities.
Innovation Solution
A recommendation server that determines the time users met by analyzing their activity information and provides a list of content related to that time, allowing for seamless content sharing between connected devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search for content to share during a call, then they can find content, but it takes a long time to search for the desired content
Solution Approach 1:
The system pre-processes and stores user activity information (location, executed content, generated content) in advance before communication occurs. When communication starts, the server quickly retrieves pre-analyzed meeting time information and recommends relevant content without requiring users to manually search, thus reducing search time while maintaining ease of operation
Solution Approach 2:
A recommendation server acts as an intermediary between users' devices and the content sharing process. The server collects activity information from multiple devices, determines meeting times by comparing location data, identifies relevant content, and presents recommendations to users. This intermediary handles the complex search and analysis work, freeing users from manual searching while enabling efficient content sharing
2Loss of information
If the system analyzes user activity information to recommend content, then content relevance is improved, but system complexity increases
Solution Approach 1:
The system divides the content recommendation task into separate functional modules: (1) activity information collection from user devices, (2) meeting time determination by comparing location data, (3) relevant content identification based on time and location, and (4) recommendation presentation. This segmentation distributes complexity across different components rather than concentrating it in one system, making the overall complexity manageable while maintaining high content relevance
Solution Approach 2:
User devices automatically generate and transmit their own activity information (location, executed content, generated content) to the server without requiring manual input from users. The server automatically performs the analysis and matching operations. This self-service approach reduces the complexity burden on users while enabling sophisticated content recommendation based on actual user behavior
Data Source
AI summary
A recommendation server is provided. The recommendation server includes a communicator configured to receive, from a first device, when a first user's input for communicating with a second user is received by the first device of the first user, a transmission request with respect to a list of a plurality of items of content related to an operation of the first device which was performed when the first user and the second user met each other; and a controller configured to determine a time when the first user and the second user met each other, based on at least one of first user's activity information of the first user and second user's activity information of the second user, and to provide, to the first device, a list of a plurality of items of content related to an operation of the first device which was performed at the determined time.