Retail Location Recommendation via Scan Event Model
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Solution Overview
Problem
Traditional recommendation systems in network-based commerce systems rely on merchant-provided inventory data, which can be outdated and inefficient, making it difficult for users to find relevant products amidst vast amounts of information.
Innovation Solution
A system that generates recommendations for retail locations based on historical product scan messages from scanning devices, building a scan event model to match product identifiers with geographic locations and providing recommendations to users searching for specific products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional recommendation systems use merchant-provided inventory data, then the system can provide product availability information, but the data becomes outdated and inefficient
Solution Approach 1:
The system enables scanning devices at retail locations to automatically send product scan messages to the network-based system. This self-service mechanism allows the retail location to autonomously update product availability data in real-time without requiring manual intervention from merchants or periodic system polls, thereby eliminating data delays and ensuring continuous accuracy.
Solution Approach 2:
The system implements a feedback loop where product scan messages from scanning devices are continuously transmitted to the network-based system, which then updates the product availability database. This real-time feedback mechanism ensures that the recommendation system always has current information about product availability at various retail locations.
2Measurement precision
If the system collects and processes large amounts of product scan data, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts only the essential information from product scan messages (product identifier and retail location identifier) and stores it in a structured scan event model. By extracting only the necessary data elements rather than processing entire raw messages, the system achieves high recommendation accuracy while minimizing processing complexity and storage requirements.
3Speed
If the system uses actual product scan data instead of merchant inventory data, then real-time recommendations are enabled, but data collection infrastructure complexity increases
Solution Approach 1:
Scanning devices at retail locations automatically and autonomously send product scan messages to the network-based system without requiring manual data entry or complex collection infrastructure. This self-service approach enables real-time data collection while keeping the infrastructure simple and easy to deploy.
Data Source
AI summary
A method and a system are disclosed for generating a recommendation of a retail location on a network-based system. For example, a system may obtain a retail location definition associated with a geographic location. The geographic location may represent the retail location. The system then builds a scan event model from product scan messages received from a plurality of scanning devices located within the geographic location. The scan event model may include one or more scan events each being associated with a product definition and the retail location definition. Next, a recommendation query from the search device is received by the system. The recommendation query may include a product identifier and a query location. The system may generate a recommendation of the retail location based on determining that the product identifier and the query location match the one or more scan events of the scan event model.