Cross-Domain Recommendation Uplift Vector Generation
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Solution Overview
Problem
Machine learning models struggle to generate accurate predictions when applied to domains different from those on which they were trained, leading to inaccurate cross-domain recommendations.
Innovation Solution
A method utilizing a multilabel classification model and an uplift model to generate an uplift vector, which predicts the likelihood of a subject being positively associated with a second domain based on information from a first domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained on data from one domain, then it can make accurate predictions within that domain, but it produces inaccurate predictions when applied to a different domain
Solution Approach 1:
The patent introduces an intermediary mapping layer that transforms features from the first domain to the second domain. This mapping layer acts as a mediator that enables the model trained on the first domain to make accurate predictions in the second domain by bridging the domain gap through feature transformation.
Solution Approach 2:
The patent changes the parameters of the machine learning model by incorporating domain-specific parameters and adjustment factors when applying the model across domains. This allows the model to adapt its behavior and predictions when moving from one domain to another, maintaining accuracy despite domain differences.
2Measurement precision
If a machine learning model is trained to be highly specialized in one domain, then it achieves high accuracy for that specific domain, but it loses the ability to generalize to other domains
Solution Approach 1:
The patent creates a universal machine learning model framework that can function across multiple domains. By incorporating a domain adaptation layer and using transfer learning techniques, the model maintains its specialized knowledge for the training domain while gaining the ability to generalize to other domains through the mapping mechanism.
Solution Approach 2:
The patent segments the machine learning model into distinct components: a core model trained on the first domain, a mapping layer for domain transformation, and an adaptation layer for cross-domain adjustments. This segmentation allows each component to specialize in its function while working together to achieve both domain-specific accuracy and cross-domain generalization.
Data Source
AI summary
A method including receiving a domain vector including a first data structure describing a first domain with which a subject interacts. A multilabel classification model is applied to the domain vector to generate a classification prediction including a classification vector. The classification vector has a second data structure describing a likelihood that a second domain, which is different than the first domain, is related to the subject. The classification prediction is based on the first domain. An uplift model is applied to the classification vector to generate an uplift value. The uplift value represents a probability that the subject is positively associated with the second domain. A vectorization algorithm is applied to the subject, the second domain, and the uplift value to generate an uplift vector including a third data structure describing a triplet of the subject, the second domain, and the uplift value. The uplift vector is returned.