Deep-Embedding Architecture for Real-Time Grocery Recommendations
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Solution Overview
Problem
Existing online grocery shopping recommendation systems fail to provide personalized and relevant item recommendations that account for user preferences and item compatibility, leading to inefficient shopping experiences due to the complexity of online grocery shopping, which is highly personal and varies in purchase frequency and product characteristics.
Innovation Solution
A large-scale deep-embedding architecture system that uses a triple embeddings model to represent users, items, and baskets, leveraging a skip-gram framework to generate personalized recommendations by learning embeddings from past purchase data and applying a complementary category filtering technique to enhance recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems are used for online grocery shopping, then implementation is simpler, but recommendation accuracy and personalization are insufficient
Solution Approach 1:
The patent segments the recommendation system into multiple independent embedding models: user embedding model, item embedding model, and basket embedding model. Each model focuses on specific aspects (user preferences, item characteristics, item compatibility) and can be trained and updated independently, reducing overall system complexity while improving recommendation accuracy through specialized processing.
Solution Approach 2:
The patent transforms the recommendation problem from traditional collaborative filtering into a multi-dimensional embedding space where users, items, and baskets are represented as vectors. This dimensional transformation enables the system to capture complex relationships (user preferences, item compatibility, basket complementarity) that are difficult to model in traditional recommendation frameworks.
2Measurement precision
If real-time inferencing is implemented for personalized recommendations, then recommendation relevance improves, but processing latency increases
Solution Approach 1:
The patent pre-trains embedding models offline using historical purchase data to learn user preferences, item characteristics, and basket patterns. These pre-trained models capture complex relationships in advance, enabling the system to perform fast real-time inference by simply computing embeddings for current user-basket combinations without retraining, thus reducing online processing latency while maintaining high recommendation relevance.
Solution Approach 2:
The patent introduces embedding vectors as intermediary representations that mediate between raw input data (user profile, basket items) and final recommendations. These embeddings compress complex information into compact vector forms, enabling efficient real-time computation and comparison while preserving the nuanced relationships learned during offline training, thus bridging the gap between accuracy and speed.
3Adaptability or versatility
If comprehensive user preferences and item compatibility are considered, then recommendation personalization improves, but computational complexity increases
Solution Approach 1:
The patent divides the complex personalization task into separate embedding models: user embedding model captures user preferences, item embedding model captures item characteristics, and basket embedding model captures item compatibility. This segmentation allows each model to specialize in one aspect, reducing the computational complexity of each individual model while collectively achieving comprehensive personalization through their combination.
Solution Approach 2:
The patent transforms the personalization problem from considering raw user preferences and item attributes directly into learning low-dimensional embedding vectors that capture these relationships. By changing the parameter representation from high-dimensional raw data to compressed embedding vectors, the system reduces computational complexity while preserving the essential information needed for personalized recommendations.
Data Source
AI summary
A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets. The triplets each include a respective first user of the users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket. The method also can include randomly sampling an anchor item from a category of items selected by a user. The method additionally can include generating a list of complementary items using a query vector associated with the user and the anchor item. The query vector is generated for the user and the anchor item using the two sets of item embeddings and the set of user embeddings. Other embodiments are disclosed.