Replacement Query Engine for Marketplace Listings
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Solution Overview
Problem
Buyers on online marketplaces often face frustration when their offers are declined, leading to discouragement and repetitive searches for alternative sellers, resulting in inefficient use of resources and increased effort in resubmitting queries.
Innovation Solution
An online marketplace system that automatically identifies and surfaces alternative listings and generates replacement queries based on item attributes, seller reputation, and acceptance history, allowing buyers to receive notifications and offers from top sellers within a limited time frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If buyers manually perform searches and resubmit offers after declined offers, then they can find alternative sellers, but it increases their effort and time consumption
Solution Approach 1:
The system pre-identifies and stores alternative listings with similar item attributes before buyers need to search. When an offer is declined, the system has already prepared replacement queries based on the original search criteria, eliminating the need for buyers to manually restart their search process.
Solution Approach 2:
The system automatically performs the search and offer resubmission process without requiring buyer intervention. The replacement query engine autonomously identifies alternative sellers, generates new offers, and presents options to buyers, allowing the system to serve itself in completing the transaction search.
2Productivity
If buyers resubmit queries manually for alternative sellers, then they can continue pursuing the item, but it consumes additional computing resources and network traffic
Solution Approach 1:
The system combines multiple separate operations (searching for alternatives, filtering by attributes, generating offers, and submitting queries) into a single automated replacement query process. This consolidation reduces redundant computing operations and network communications that would occur if buyers manually performed each step.
Solution Approach 2:
The system creates replacement queries by copying and adapting the original search parameters and offer structure. Instead of requiring buyers to manually recreate search queries and offers, the system generates copies modified with alternative seller information, significantly reducing the computational and network resources required.
3Adaptability or versatility
If the system provides multiple alternative offers to buyers, then buyers have more options, but it increases system complexity
Solution Approach 1:
The system segments the complex task of finding alternative sellers into distinct functional modules: attribute extraction from original listings, similarity matching algorithm, seller identification, and query generation. This modular segmentation manages system complexity by organizing the versatility-providing functionality into manageable, independent components.
Data Source
AI summary
A server receives an initial query that identifies a first item listed by a first user account and an initial query value for the first item. The server provides the initial query to a first computing device associated with the first user account and receives a first response to the initial query from the first computing device. The first response indicates a rejection of the initial query value. In response to the rejection, the server identifies a second item listed by a second user account. The second item includes a shared attribute with the first item. The server substitutes the initial query with a replacement query that identifies the second item and the initial query value for the first item. The replacement query is provided to a second computing device associated with the second user account.