Purchase Embeddings for Multi-Dimensional Recommendation Accuracy
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Solution Overview
Problem
Current search engines provide minimally useful results for purchase recommendations as they rely on simple word matching, failing to consider user intent, regional preferences, and correlations between items based on purchasing history, leading to irrelevant suggestions from large box stores rather than preferred vendors.
Innovation Solution
The introduction of 'purchase embeddings' – data records in a multi-dimensional space that represent and describe purchases, allowing for the calculation of similarities between items based on various characteristics, including past buying history, regional differences, and vendor preferences, enabling more accurate and relevant shopping recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If simple word matching is used for search results, then search speed and simplicity are improved, but recommendation accuracy and relevance to user intent deteriorate
Solution Approach 1:
The patent transforms item descriptions from simple text labels into multi-dimensional numerical vectors (embeddings) that capture semantic meaning. This parameter transformation enables the system to move from exact string matching to similarity-based matching, improving recommendation accuracy while maintaining computational efficiency through vector operations.
Solution Approach 2:
The patent introduces a new dimension of representation by converting scalar text matches into multi-dimensional vector embeddings. Each item is represented as a point in N-dimensional space, allowing the system to capture complex relationships between items across multiple features simultaneously, thereby improving recommendation relevance.
2Measurement precision
If purchase embeddings in multi-dimensional space are used, then recommendation accuracy and personalization are improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent creates compressed vector representations (embeddings) that copy the essential semantic characteristics of items into a lower-dimensional numerical form. These embeddings serve as efficient proxies for the original complex item data, enabling fast similarity computations without processing the full item descriptions repeatedly.
Solution Approach 2:
The system pre-computes and stores item embeddings in advance, transforming complex item descriptions into numerical vectors before they are needed for recommendations. This preliminary action eliminates the need for complex text processing during query time, significantly reducing computational complexity when generating recommendations.
3Adaptability or versatility
If correlations based on purchasing history are analyzed, then personalization and user preference matching are improved, but data processing time and storage requirements increase
Solution Approach 1:
The patent creates compressed vector representations (embeddings) that copy the essential semantic characteristics of items into a lower-dimensional numerical form. These embeddings serve as efficient proxies for the original complex item data, enabling fast similarity computations without processing the full item descriptions repeatedly.
Solution Approach 2:
The system continuously learns from user purchasing behavior by updating item embeddings based on observed correlations. When users purchase items together, the system reinforces the relationship between those items in the embedding space, improving personalization over time while maintaining efficient query performance through the pre-computed vector representations.
Data Source
AI summary
Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on a new descriptor referred to as “purchase embeddings.” The purchase embedding may include a tuple in a new multi-dimensional search space for describing and tracking purchases of goods and services. Recommendations may be based on a distance between tuples in the search space, which provides a measure of similarity between items represented by the tuples.