Dynamic Browse Facet Ranking via Composite Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current eCommerce websites face challenges in efficiently ranking and updating facets on browse pages due to manual ordering, which is time-consuming and not scalable to respond to customer behavior.
Innovation Solution
A system and method that utilize processing modules and storage modules to analyze user activity, determine composite scores for facets based on click, add-to-cart, and order scores, and rank facets for display, enabling dynamic and responsive facet ordering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual ordering of facets is used, then facets can be displayed in a fixed order, but the system cannot respond quickly to customer behavior changes and the process is time-consuming
Solution Approach 1:
The system automatically calculates composite scores for facets based on user interaction data (clicks, adds to cart, orders) and dynamically reorders facets without manual intervention. The processing modules continuously analyze user behavior and update facet rankings, enabling the system to serve itself in maintaining optimal facet ordering.
Solution Approach 2:
The system implements a feedback loop where user interactions with facets are continuously monitored and fed back into the scoring mechanism. The composite scores are recalculated based on accumulated user behavior data, and facet ordering is adjusted accordingly, creating a responsive system that adapts to changing customer preferences.
2Productivity
If manual ordering of facets is used, then the implementation is simple, but the process is not scalable
Solution Approach 1:
The facet ranking system is divided into independent processing modules, each responsible for calculating specific components of the composite score (click score, add-to-cart score, order score). This modular architecture allows the system to scale by adding or adjusting individual modules without redesigning the entire system.
Solution Approach 2:
The system uses configurable parameters and weights for different user interaction types (clicks, adds to cart, orders) that can be adjusted without changing the underlying system architecture. This allows the system to adapt to different business requirements and scale across various product catalogs while maintaining a consistent processing framework.
Data Source
AI summary
In some embodiments, a method can comprise receiving a query and determining one or more results for the query. In many embodiments, the one or more results can comprise one or more shelves, each shelf of the one or more shelves can comprise one or more facets, and each facet of the one or more facets can comprise one or more items. In various embodiments, the method can further comprise facilitating display of at least a portion of the one or more results for the query by facilitating display of at least the portion of the one or more results in a ranked order. In many embodiments, displaying the portion of the one or more results in a ranked order can comprise determining a composite score for each facet of the one or more facets of each shelf of the one or more shelves. Other embodiments of related methods and systems are also provided.