Product Graph for Context-Aware Substitute Ranking
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Solution Overview
Problem
Users of online retail systems face challenges in finding relevant product substitutes as existing systems rely solely on expressed search criteria, lacking consideration for contextual factors, leading to a trial-and-error process and suboptimal results.
Innovation Solution
A method utilizing a machine learning module to construct a product graph that incorporates attributes and contextual information, determining substitutes by projecting the requested product onto this graph, and ranking them based on similarity scores for accurate and context-aware recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies solely on expressed search criteria to find product substitutes, then the search process is simple, but the relevance and accuracy of the substitutes are reduced
Solution Approach 1:
The patent introduces a product graph as an intermediary structure that connects products through shared attributes and contexts. This graph serves as a mediator between the simple search criteria input and the complex contextual analysis needed to determine substitute relevance, enabling accurate recommendations without requiring the system to directly process all contextual factors
Solution Approach 2:
The system pre-processes and stores product attribute relationships in a product graph structure before search operations. By organizing product data with attributes and contextual relationships in advance, the system enables fast and accurate substitute identification during search without performing complex real-time analysis
2Productivity
If the system uses trial-and-error search process, then users can find substitutes, but the time required for product discovery increases
Solution Approach 1:
The system implements feedback by analyzing user interactions with substitute recommendations and using this information to refine future substitute suggestions. The product graph structure allows the system to learn from user behavior patterns and improve substitute relevance over time, reducing the need for repeated trial-and-error searches
Solution Approach 2:
By pre-organizing products in a graph structure with attributes and relationships established beforehand, the system enables immediate retrieval of relevant substitutes without requiring users to perform multiple search iterations, thus eliminating trial-and-error time
3Measurement precision
If the system provides substitutes without contextual information, then the search process is faster, but the accuracy of matching user expectations decreases
Solution Approach 1:
The patent segments the product information system into distinct components: products, attributes, and contexts, organized in a product graph. This segmentation allows the system to selectively incorporate relevant contextual information for each substitute recommendation, improving match accuracy without being overwhelmed by excessive information
Data Source
AI summary
A system can include one or more processors and non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform a method for determining one or more substitutes for a product requested in an inquiry from a user and the order of the one or more substitutes to be transmitted to the user. The method can comprise: receiving an inquiry from a user, wherein the inquiry comprises an image and a text describing a requested product; extracting one or more visual attributes of the requested product based on the image; extracting one or more textual attributes of the requested product based on the text; determining a first projected requested product, in a product graph relating to a domain of the requested product, based on the one or more visual attributes of the requested product; determining a second projected requested product in the product graph based on the one or more textual attributes of the requested product; and determining one or more substitutes for the requested product. In this embodiment, the one or more substitutes for the requested product can be determined by: determining one or more first candidates, each first candidate of the one or more first candidates existing in both the product graph and an inventory database and comprising a visual distance between the first projected requested product and the each first candidate in the product graph; determining one or more second candidates, each second candidate of the one or more second candidates existing in both the product graph and the inventory database and comprising a textual distance between the second projected requested product and the each second candidate in the product graph, the one or more substitutes comprising the one or more first candidates and the one or more second candidates; determining a score of a candidate substitute of the one or more substitutes based on a visual distance and a textual distance of the candidate substitute of the one or more substitutes; and sorting the one or more substitutes based on the score of the candidate substitute of the one or more substitutes. In this embodiment, after the one or more substitutes are determined and sorted, this method can further comprise: in response to receiving the inquiry from the user, automatically transmitting to the user higher ranked ones of the one or more substitutes. Additionally, in this embodiment, the product graph can be dynamically generated by a machine learning module based on information about the domain of the requested product; and the product graph can comprise: (a) one or more product nodes, the one or more product nodes comprising the one or more substitutes; (b) one or more entity nodes, the one or more entity nodes comprising the one or more visual attributes and the one or more textual attributes of the requested product; (c) one or more entity-entity connections between each pair of related entity nodes of the one or more entity nodes; (d) one or more product-entity connections between a product node of the one or more product nodes and an entity node of the one or more entity nodes, wherein the entity node is related to the product node; and (e) one or more product-product connection between a first product node of the one or more product nodes and a second product node of the one or more product nodes, wherein the first product node and the second product node are determined to be substitutable by or complimentary of each other. Other embodiments are disclosed.