Tag Scoring System for E-commerce Product Search Relevance
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Solution Overview
Problem
E-commerce websites face challenges in efficiently navigating and locating desired products due to haphazard listing of search results from tag searches, where relevant products may be buried beneath less desirable ones.
Innovation Solution
Implementing tag scoring that orders search results based on factors such as the number of times an item is tagged, user votes on relevance, quality scores, customer reviews, sales rank, and availability, ensuring that the most relevant items are listed prominently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tag search is implemented to categorize products, then product search capability is improved, but product listing order becomes haphazard and difficult to navigate
Solution Approach 1:
The patent applies parameter changes by introducing a tag score parameter that quantifies the relevance between tags and items. This score is calculated based on multiple factors including user votes, co-occurrence frequency, and tag hierarchy relationships. By changing the listing order parameter from arbitrary to score-based, the system resolves the contradiction between search versatility and navigation ease
Solution Approach 2:
The system implements feedback mechanisms where user interactions (votes, clicks, purchases) continuously refine tag scores. User votes on tag relevance directly feed into score calculations, and these updated scores immediately affect listing orders. This closed-loop feedback system dynamically optimizes both search capability and listing quality based on actual user behavior
2Measurement precision
If multiple tag factors are considered for scoring, then product relevance accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the tag scoring system into distinct modular components: user vote processing module, co-occurrence analysis module, tag hierarchy evaluation module, and weight adjustment module. Each module independently calculates specific aspects of relevance, and their results are combined through weighted summation. This segmentation reduces overall system complexity while maintaining comprehensive relevance assessment
Solution Approach 2:
The tag scoring system is designed as a universal multi-functional mechanism that handles diverse relevance factors through a unified framework. The same core scoring algorithm processes user votes, click-through rates, purchase data, and tag relationships simultaneously. This universal approach avoids creating separate complex systems for each factor, reducing overall complexity while achieving high measurement precision
Data Source
AI summary
Tag scoring for elements associated with a common tag enables a user to conduct a tag search and view a listing of elements associated with the tag. The listing can be ordered by each element's tag score. To create such a tag score, users may associate an element with one or more tags that characterize the element. A tag score may then be assigned to this element for each assigned tag and may be based on a multitude of factors. These factors may include a number of times that the element has been tagged with the assigned tag and users' votes on the accuracy of the assigned tag to the element. Tag-scoring factors may include, among others, an age of a tag associated with the element, a click rate for the element, a customer review of the element, a sales rank for the element, or an availability of the element.