Neural Network Quadruplet Ranking for Complementary Item Recommendations
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Solution Overview
Problem
Electronic platforms face challenges in accurately and efficiently providing item recommendations, especially when dealing with large datasets, as existing methods struggle to identify highly relevant items in real-time or near real-time.
Innovation Solution
A system utilizing a neural network architecture with a quadruplet network, ranking network, and re-ranking network to identify and rank complementary items based on anchor items, generating scores and customizing recommendations using user profiles for improved relevance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation methods are used to identify relevant items in large datasets, then the system can handle basic recommendation tasks, but the accuracy of identifying highly relevant items deteriorates
Solution Approach 1:
The patent segments the recommendation task into multiple processing stages: initial filtering to reduce dataset size, then detailed analysis on the filtered subset. This multi-stage approach allows the system to maintain high accuracy by applying complex analysis only to relevant candidates rather than the entire large dataset.
Solution Approach 2:
The patent introduces intermediary processing layers including feature extraction modules and scoring mechanisms that act as mediators between the raw dataset and final recommendations. These intermediaries transform and pre-process data to improve the accuracy of subsequent analysis stages.
2Measurement precision
If complex analysis methods are applied to identify highly relevant items accurately, then the precision of recommendations improves, but the time required to provide recommendations increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing data, extracting features, and creating item profiles before the actual recommendation query. This advance preparation enables faster real-time recommendations while maintaining high precision, as the complex analysis work is done beforehand.
Solution Approach 2:
The recommendation process is divided into time-efficient segments: quick initial filtering using pre-computed features, followed by more detailed analysis only on shortlisted candidates. This segmentation reduces overall processing time while preserving recommendation precision.
3Speed
If real-time recommendations are provided to users, then the responsiveness and user experience improve, but the computational complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary computations including data cleaning, feature extraction, and item profiling during off-peak times or in batch processes. This allows real-time recommendation generation to focus only on the final ranking and selection steps, reducing computational complexity during user interactions while maintaining responsiveness.
Solution Approach 2:
The patent applies partial analysis by focusing computational resources on the most promising candidates identified through quick initial filtering. Rather than analyzing all items equally, the system performs detailed analysis only on a small subset of high-potential recommendations, reducing overall computational requirements while maintaining speed.
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving an input identifying an anchor item; determining, using a quadruplet network associated with a neural network architecture, one or more item categories corresponding to complementary items associated with the anchor item; generating, using a ranking network associated with the neural network architecture, scores for the complementary items included in the one or more item categories; generating, using the ranking network associated with the neural network architecture, first ranking results for the complementary items based, at least in part, on the scores; and selecting one or more of the complementary items to be displayed based, at least in part, on the first ranking results. Other embodiments are disclosed herein.