Attribute-Based Item Recommendation Using Profile Matching
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Solution Overview
Problem
Current data search methods are inefficient and inaccurate in recommending items from large and varied datasets, as they rely on rough categories and cannot accurately fulfill search demands of item requesting agents in a timely manner.
Innovation Solution
A computer-implemented method and system for generating attribute profiles of selectable items based on attribute elements, creating an attribute requirement description from historical search data of item requesting agents, and determining recommended items that match these profiles, using techniques like M-dimensional Euclidean Distance and Pearson Correlation Coefficient for precise matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search tools and approaches are used to search for items from large and varied datasets, then the search process can be completed, but the search time is excessive and the search results are low quality
Solution Approach 1:
The system performs preliminary actions by pre-generating attribute profiles for all selectable items in the dataset before actual search queries are made. These profiles contain extracted attribute elements and their relationships, so when a search request arrives, the system can quickly match query attributes against pre-computed profiles rather than analyzing items in real-time, thus reducing search time while maintaining high result quality
Solution Approach 2:
The invention replaces conventional keyword-based mechanical search methods with an attribute-based semantic matching system. Instead of relying on text matching algorithms, the system uses attribute element extraction and profile comparison mechanisms that understand item characteristics and requirements at a semantic level, improving both search speed and result relevance
2Adaptability or versatility
If the dataset size and variety are increased to provide more item options, then the selection capability is improved, but the difficulty of identifying required data increases
Solution Approach 1:
The system segments the complex task of item search into distinct components: attribute element extraction from items, attribute requirement extraction from queries, profile generation, and matching. By breaking down items into their constituent attribute elements and creating structured profiles, the system makes it easier to identify and compare required data even in large and varied datasets
Solution Approach 2:
The attribute profile serves as an intermediary representation between the raw item data and the search query. Instead of directly comparing complex item data with search requirements, the system uses attribute profiles as a standardized intermediate format that simplifies the matching process, reducing the difficulty of identifying required data across diverse item types
Data Source
AI summary
Embodiments of the present disclosure relate to attribute-based selectable item recommendation. A computer-implemented method is disclosed. A computer generates respective attribute profiles of selectable items based on one or more attribute elements included in the selectable items. The profiles include at least one attribute elements respectively correspond to the selectable items. The computer generates an attribute requirement description of an item requesting agent based on a historical search dataset of the item requesting agent. The attribute requirement description includes at least part of the one or more attribute elements. The computer determines recommended selectable items included in the selectable items for the item requesting agent based on the attribute profiles of the selectable items and the attribute requirement description of the item requesting agent. The attribute profiles of the recommended selectable items are suitable with the attribute requirement description based on the one or more attribute elements.