Social Network Recommendation List Using Two-Tuple Ranking
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Solution Overview
Problem
Users face significant challenges in manually recommending information to others within large social networks, as they need to manually compare and forward information to multiple users, which is time-consuming and inefficient, especially when dealing with large volumes of information and numerous recipients.
Innovation Solution
A method and apparatus that build a recommendation list using two-tuples, each containing a target user name and information item, which are then ranked based on features such as match degrees and closeness, allowing users to quickly and accurately recommend information by selecting relevant two-tuples from the list.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually recommend information to multiple users in a large social network, then they can provide personalized information sharing, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The system pre-calculates and stores match degrees between users and information items, as well as closeness degrees between user pairs, before recommendation is needed. When a user wants to share information, the recommendation list is quickly generated using these pre-computed values, avoiding real-time complex calculations and significantly reducing the time required for information recommendation.
2Ease of operation
If users manually select and forward information to multiple users, then they can control information distribution, but the operational complexity and effort required increase
Solution Approach 1:
The system automatically generates the recommendation list by computing match degrees and closeness degrees, and presents sorted recommendations to the user. The user simply needs to review and confirm the automatically generated list, rather than manually analyzing and selecting each recipient. This self-service approach significantly reduces operational complexity while maintaining control over information distribution.
3Measurement precision
If the system provides automated recommendation lists with multiple factors, then recommendation accuracy improves, but the computational requirements and processing time increase
Solution Approach 1:
The system pre-computes and stores match degrees between users and information items, and closeness degrees between user pairs, in a database. These pre-computed values are reused when generating recommendation lists, avoiding repeated complex calculations. This approach maintains high recommendation accuracy while significantly reducing real-time computational resource consumption.
Data Source
AI summary
A method and an apparatus for recommending information to users within a social network. The method builds a recommendation list with at least one two-tuple, where each two-tuple comprises a target user name and an information item and ranks the recommendation list by using two-tuples in the recommendation list as a basic unit. By selecting a two-tuple in the recommendation list, the user can recommend a corresponding information item to a user represented by a target user name. An apparatus is also provided by using a builder for building for a user a recommendation list comprising at least one two-tuple and a sorter for ranking the recommendation list by using two-tuples in the recommendation list as a basic unit, such that, by selecting a two-tuple in the recommendation list.