Context-Aware Content Recommendation Server Using Position and Access History
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Solution Overview
Problem
Current content sharing systems fail to effectively recommend and share content based on the position and situation of devices, leading to inadequate content delivery that matches user preferences.
Innovation Solution
A content recommendation server that utilizes context information, including position, access history, and user information, to recommend content to devices positioned in specific regions, ensuring that content is shared and recommended based on context-aware criteria such as time, weather, and user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content is shared without considering position and access history, then system complexity is reduced, but content recommendation accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing position information and content access history in advance. When a user requests content, the server has already processed and organized this contextual data, enabling rapid and accurate content matching without complex real-time computations.
Solution Approach 2:
The patent introduces a content recommendation server as an intermediary between content providers and users. This server handles the complex tasks of analyzing position information, access history, and user preferences, thereby shielding end users from system complexity while delivering accurate recommendations.
2Productivity
If context information is collected and analyzed, then content sharing effectiveness is improved, but information processing load increases
Solution Approach 1:
The patent extracts only the most relevant contextual information (position data and access history) needed for content recommendation, rather than processing all possible user data. This selective extraction reduces processing load while maintaining recommendation effectiveness.
Solution Approach 2:
The system changes parameters by focusing on specific dimensions of context information (geographic location, temporal patterns, content categories) rather than analyzing all possible attributes. This parameter selection optimizes the balance between recommendation quality and processing requirements.
Data Source
AI summary
A method and system for sharing content by using context information. A content recommendation server for recommending content to a device includes a context information receiving unit configured to receive position information of a first device from the first device. The server also includes a content registering unit configured to receive a request for registration of content from the first device and register the content together with the received position information. The server further includes a content recommendation unit configured to recommend the registered content to a second device in response to determining that the second device is within a region associated with the received position information of the first device. The region is allocated to the first device and a size of the region is determined based on a number of times that the registered content is provided to another device.