Edge-Weighted Item Recommendation Models With Low-Latency Retail Inference
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Solution Overview
Problem
Existing retail systems face challenges in providing effective item recommendations due to dynamic store layouts and lack of digitized item location mapping, leading to suboptimal user experiences and system latency.
Innovation Solution
A system utilizing an edge weight model for item recommendations, based on an item interaction graph with nodes and edges weighted by selection sequences, generates dense representations to provide personalized suggestions without requiring sensors or computer vision, reducing backend overload and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-based solutions and computer vision are used for item recommendations, then recommendation accuracy can be improved, but system complexity and cost increase significantly
Solution Approach 1:
The patent extracts and eliminates the complex sensor-based detection systems and computer vision components from the recommendation system. Instead of using physical sensors to track items, the system uses lightweight mobile application data collection to capture item selection sequences, thereby achieving recommendation functionality without the complexity of hardware sensors and vision systems.
Solution Approach 2:
The patent replaces the mechanical sensor-based detection system with a software-based solution using mobile applications. The mobile app captures item selection data through user interaction rather than physical sensors, substituting complex hardware mechanisms with simpler software-based data collection methods that achieve the same recommendation goal.
2Measurement precision
If digitized mapping and sensor-based solutions are implemented, then item location tracking accuracy improves, but system cost and complexity increase
Solution Approach 1:
The patent uses inexpensive mobile application instances rather than expensive permanent sensor infrastructure. The mobile app serves as a temporary, lightweight data collection tool that runs on existing user devices, eliminating the need for costly digitized mapping systems and permanent sensor installations while still achieving accurate item location and selection tracking.
Solution Approach 2:
The system leverages the user's own mobile device and existing application to perform data collection functions. The mobile app utilizes the device's built-in capabilities (GPS, accelerometer, user interface) to track item selections without requiring external sensors or complex infrastructure, making the system self-sufficient and cost-effective.
3Ease of operation
If traditional recommendation systems with digitized mapping are used, then recommendation capability is provided, but backend system latency and overload increase
Solution Approach 1:
The patent segments the recommendation system into distributed mobile application instances rather than relying on a centralized backend system. Each mobile app independently collects item selection data and generates recommendations locally, dividing the computational load across multiple user devices rather than concentrating it in a single backend server, thereby reducing latency and preventing overload.
Solution Approach 2:
The mobile application performs data collection and preliminary processing of item selection sequences directly on the user device before transmitting any data to the backend. This preliminary action at the edge reduces the amount of data that needs to be processed centrally and enables faster recommendation generation by utilizing local computational resources.
Data Source
AI summary
In some embodiments, systems and methods are provided herein useful to generating item recommendations. In some embodiments, a system for item recommendation may include a memory storing an item recommendation model trained using dense representations for items, the dense representations generated based on an item interaction graph including nodes corresponding to items for sale in a retail facility, edges connecting the nodes, and edge weights for the edges, wherein the edge weights are related to item selection sequence of the items, and a control circuit coupled to the memory, the control circuit configured to receive an indication of an item selection by a user in the retail facility, generate a recommendation for a suggested next item based on using the item selection as an input of the item recommendation model, and cause the one or more suggested next best items to be presented to the user for potential selection.