User Interest Matching via Vector Space Modeling
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Solution Overview
Problem
Traditional pairing or matching algorithms struggle to effectively match users with near or dissimilar attributes that have been found compatible based on historical matches, particularly when users enter freeform text that includes different seasonal activities or preferences.
Innovation Solution
An evaluation system that preprocesses freeform text data by removing non-letter characters and stop words, stems words, and generates user interest groups based on their indicativeness, allowing for the creation of user interest group membership vectors that assess compatibility between users, even with misspelled or culturally specific terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pairing algorithms search for common words in freeform text entries, then literal text matches can be found, but users with near-matching or dissimilar attributes that have been found compatible based on historical matches cannot be effectively paired
Solution Approach 1:
The patent transforms freeform text into structured numerical vectors by converting words into numerical representations and aggregating them into user profile vectors. This parameter transformation enables the system to move from exact string matching to geometric similarity calculations, allowing users with near-matching or dissimilar attributes to be effectively paired based on historical compatibility data.
Solution Approach 2:
The patent replaces traditional text-based mechanical search algorithms with a mathematical vector space model. Instead of searching for common words using string comparison, the system uses vector operations to calculate similarity between user profiles, enabling more flexible and accurate matching that captures nuanced relationships between user attributes.
2Adaptability or versatility
If freeform text is used to capture user interests and preferences, then user vocabulary flexibility and breadth are improved, but effective and efficient use of such text in pairing processes becomes impeded
Solution Approach 1:
The patent extracts meaningful information from freeform text by converting words into numerical vectors and aggregating them into structured user profile vectors. This extraction process separates the essential semantic content from the variability of user vocabulary, enabling efficient processing while preserving the flexibility of freeform text input.
Solution Approach 2:
The patent transforms unstructured freeform text into structured numerical vectors, changing the parameter representation from variable-length strings to fixed-dimensional vectors. This transformation enables efficient mathematical operations while maintaining the vocabulary flexibility of freeform text, as the vectorization process captures semantic meaning regardless of the specific words used.
3Loss of information
If user profiles include freeform text entries with different seasonal activities or cultural terms, then user preferences can be accurately expressed, but traditional matching algorithms fail to recognize compatible interests across different contexts
Solution Approach 1:
The patent replaces traditional text-based comparison methods with a vector space model that calculates similarity based on numerical representations. This substitution enables the system to assess interest compatibility across different contexts by measuring the geometric relationship between vector profiles, rather than relying on exact word matches, thus recognizing compatible interests even when expressed with different seasonal activities or cultural terms.
Solution Approach 2:
The patent transforms qualitative user preferences expressed in freeform text into quantitative vector representations. This parameter change enables the system to assess compatibility by comparing vector similarities, preserving the accuracy of user preference expression while enabling precise measurement of interest compatibility across diverse cultural and contextual expressions.
Data Source
AI summary
Websites operated by relationship building service providers collect information from users for use in identifying candidate matches for a user. At least a portion of the information may include freeform text entered by the user to identify the user's interests. The freeform text entered by users may be preprocessed and a user interest group model formed using a generative Natural Language Process that is applied to determine user interest groups from the freeform text. Such user interest groups identify related words based on the frequency of their occurrence and relationship within the freeform text. Individual user interest group membership vectors for each user included in a number of users may be determined using the user interest group model. At least one compatibility aspect between users may then be assessed based on the similarity or distance between the user interest group membership vectors associated with each user.