Cross-Type Item Embeddings for Adaptive Software Recommendations
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Solution Overview
Problem
Existing techniques for targeted item recommendations in software applications are inflexible and lack accuracy, particularly in dynamic environments where items and selection conditions frequently change, such as in tax preparation software, due to the need for large amounts of specific training data and inflexible rule-based or separate machine learning models.
Innovation Solution
A machine learning model is trained to recognize latent relationships among different item types using embeddings, allowing it to determine recommendations based on user features and item embeddings without explicit item identifiers, enabling adaptability and accuracy in dynamic contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate machine learning models are trained for each item type, then recommendation accuracy for specific items can be improved, but the system complexity and training data requirements increase significantly
Solution Approach 1:
The patent applies universality by training a single machine learning model to handle multiple item types (e.g., tax deductions, tax credits, forms) rather than creating separate models for each item type. The model uses a unified architecture that processes different item types through their respective feature sets and embedding spaces, enabling one model to perform recommendations across diverse item categories without requiring separate specialized models for each.
2Ease of manufacture
If fixed rules are used for recommendations, then the system is simpler to implement, but adaptability to changing items and conditions is poor
Solution Approach 1:
The patent implements dynamics by replacing static fixed rules with a dynamic machine learning model that automatically adapts to changing items and conditions. The model continuously learns from user interactions and feedback, adjusting its recommendations based on evolving patterns in user behavior and changing tax regulations. This dynamic approach allows the system to adapt to new item types and changing conditions without requiring manual rule updates.
3Measurement precision
If large amounts of training data specific to each item type are used, then recommendation accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent applies merging by combining training data from multiple item types into a single unified training dataset. Instead of training separate models requiring large dedicated datasets for each item type, the system merges data across different item types (tax deductions, tax credits, forms) into one training corpus. This allows the model to learn transferable patterns and relationships that improve recommendation accuracy while reducing the total amount of training data needed compared to separate specialized models.
4Measurement precision
If existing machine learning techniques are used with separate models for each item type, then specific item recommendations can be made, but the system cannot effectively handle changing and growing item lists
Solution Approach 1:
The patent implements parameter changes by using embedding spaces that can dynamically represent items with varying characteristics. The model adjusts embedding parameters and weightings based on new item types and changing conditions, allowing it to adapt to an ever-growing and changing item list. The embedding representations are continuously refined based on user interactions and feedback, enabling the system to maintain high recommendation precision while adapting to new items without requiring model retraining from scratch.
Data Source
AI summary
A method for automatically recommending items in a software application. Embodiments include retrieving attributes of a user of the software application and retrieving a machine learning model that has been trained through a supervised learning process based on labeled training data indicating whether users represented by user features historically selected, within the software application, first items of a first item type and second items of a second item type. In certain embodiments, the machine learning model is configured, as a result of the supervised learning process, to recognize latent relationships between the first items of the first item type and the second items of the second item type based on distances between embeddings. Embodiments include providing inputs to the machine learning model based on the attributes of the user and receiving, in response, indications of one or more recommended items of the first item type or the second item type.