Graph-Based Semantic Analysis for Item Tag Association
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Solution Overview
Problem
Online merchant websites face challenges in effectively recommending items and customizing search results based on user preferences due to the lack of efficient methods for analyzing and utilizing user-generated tags to discover item associations and user characteristics.
Innovation Solution
The implementation of graph-based semantic analysis using bipartite and tripartite graphs to analyze tags assigned to items, which allows for the discovery of associations between items, tags, and entities, enabling personalized recommendations and customized search results by generating ranked lists of associated tags and items based on user interactions and tag frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation methods are used, then the system is simple to implement, but it cannot effectively analyze user preferences or discover item associations
Solution Approach 1:
The patent introduces graphs as an intermediary structure to represent relationships between items, tags, and entities. The graph data structure serves as a mediator that transforms raw tag data into meaningful association patterns, enabling precise user preference analysis without requiring complex direct processing algorithms.
Solution Approach 2:
The patent replaces traditional mechanical search and filtering mechanisms with graph-based semantic analysis. Instead of using simple keyword matching or categorical filtering, the system uses graph traversals and algorithms to discover semantic associations between items based on user-generated tags, achieving more precise recommendations.
2Quantity of substance
If more tags are collected from users, then the amount of data for analysis increases, but the difficulty of processing and extracting meaningful associations increases
Solution Approach 1:
The patent segments the tag data into structured graph components, separating items, tags, and entities into distinct nodes with defined relationships. This segmentation allows the system to process large volumes of tags by breaking them down into manageable graphical representations that can be systematically analyzed through graph algorithms.
Solution Approach 2:
The patent creates a graphical representation (copy) of the tag relationships that simplifies the complex data structure. By transforming the raw tag data into a graph model, the system can easily detect associations and patterns without processing the full complexity of the original data, making analysis more manageable despite the large quantity of tags.
3Measurement precision
If the system analyzes all user interactions and tags, then the recommendation accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary graph construction and pre-computation of item associations during off-peak times or in batch processes. By preparing the graph structure and calculating key associations in advance, the system reduces real-time processing requirements, allowing accurate recommendations to be generated faster without consuming excessive computational resources during active user interactions.
Data Source
AI summary
The use of graph-based semantic analysis with respect to items and tags may enable the discovery of the characteristics of items. A tag collection component may initially obtain a corresponding set of tags for each item of multiple items. A graph generation component may then generate a graph that includes a corresponding item node for each item and a corresponding tag node for each tag cluster of tags. Each item node in the graph may be connected to each of one or more tag nodes by a respective edge. Subsequently, following assignment of a label to each tag node, a graph evaluation component may execute a random walk algorithm on the graph. The execution of the random walk algorithm may provide a corresponding ranked list of tags for each item or a corresponding set of correlated tags for each tag.