User Profile Matching Through Ranked Lists and Keywords
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Solution Overview
Problem
Conventional networking systems struggle to accurately match user profiles due to the sheer quantity of data and diverse interests of users, which are not accurately represented by pre-defined categories.
Innovation Solution
A method and system for matching user profiles by allowing users to create customized lists with titles and keywords, and then identifying similar lists and profiles based on title and item similarities, sentiment analysis, and rank-based comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-defined categories are used to represent user interests, then the system structure is simple and easy to implement, but the accuracy of user profile matching deteriorates due to inability to capture diverse and granular user interests
Solution Approach 1:
The patent segments user interests into multiple granular dimensions by allowing users to create customized lists with specific items and rankings. Instead of using broad pre-defined categories, the system divides user profiles into collections of ranked items that can be compared element-by-element, enabling precise matching while maintaining systematic organization.
Solution Approach 2:
The patent changes the parameters of user representation from fixed categorical labels to dynamic ranked lists with multiple attributes (items, rankings, titles, keywords). This parameter transformation allows for more nuanced comparison metrics including item overlap, ranking similarity, and title/keyword matching, thereby improving matching accuracy without sacrificing implementability.
2Measurement precision
If customized lists with multiple attributes are used for user profiles, then matching accuracy is improved, but the complexity of data processing and comparison increases
Solution Approach 1:
The patent segments the complex matching process into distinct modular components: title similarity comparison, keyword matching, item overlap analysis, and ranking similarity calculation. Each component handles a specific aspect of comparison independently, reducing overall system complexity while achieving comprehensive and accurate user profile matching.
3Measurement precision
If comprehensive user data is collected to improve matching accuracy, then the quality of user profiles increases, but the quantity of data to be processed increases leading to longer computation time
Solution Approach 1:
The patent applies partial action by implementing a multi-stage filtering process. First, title and keyword similarities are computed to quickly identify potentially matching profiles. Then, item overlap and ranking comparisons are performed only on this reduced subset. This approach processes comprehensive user data for quality matching while minimizing overall computation time by avoiding full pairwise comparisons of all profiles.
Data Source
AI summary
Systems and methods for list matching and/or user matching using lists is described. A method includes receiving first data associated with a first user profile including a first set of ranked items and a first title having one or more keywords. The method further includes identifying second data associated with a second user profile including a second set of ranked items and a second title including one or more keywords. The first title and the second title are within are threshold proximity based on the keywords of the first title and the second title. The method further includes determining that the first set of ranked items is most similar to the second set of ranked items by performing a rank-weighted similarity comparison. The method further includes providing a rank-ordered list for presentation on the user device.