Personality Feature Extraction for Personalized Offer Prediction
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Solution Overview
Problem
It is technically challenging to objectively and automatically combine personality, social style, and demographic factors with a user's purchasing history to accurately generate personalized offers for products or services without human intervention.
Innovation Solution
A machine learning approach is implemented to quantify user psychometric traits by extracting personality features from images, analyzing digital footprints, and scoring social style and demographic features, which are then combined with historical purchase data to train machine learning models for generating personalized offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personality features are extracted from images and combined with purchasing history to generate personalized offers, then the accuracy of customer behavior prediction is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex prediction task into multiple independent components: personality feature extraction from images, social style feature extraction from text, demographic feature extraction, and purchase history analysis. Each component processes a specific type of data independently, and their results are combined to form the final prediction. This segmentation reduces system complexity by making each subsystem manageable and independently optimizable.
Solution Approach 2:
The patent introduces intermediate processing layers that act as mediators between raw data and final predictions. Personality features, social style features, and demographic features serve as intermediate representations that bridge the gap between raw input data (images, text, purchase history) and the final customer behavior prediction. These intermediaries simplify the overall system by creating structured intermediate states that are easier to process and combine.
2Adaptability or versatility
If multiple data sources including images and social media are analyzed to extract personality and social style features, then the personalization quality of offers is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary extraction of personality features, social style features, and demographic features from various data sources before the actual offer generation process. By pre-processing and storing these extracted features, the system avoids re-extracting them for each offer recommendation, significantly reducing processing time during deployment while maintaining high personalization quality.
Solution Approach 2:
The patent implements a feature selection mechanism that identifies and processes only the most relevant features for each specific prediction task. Rather than analyzing all possible features from images and social media data equally, the system selectively processes partial sets of features based on their relevance to the current context, reducing overall processing time while maintaining personalization quality.
3Extent of automation
If machine learning models are trained on extracted features to automatically generate offers, then human intervention is eliminated, but the initial setup and training complexity increases
Solution Approach 1:
The system implements self-service mechanisms where the machine learning models automatically extract features, train on the extracted data, and generate offers without human intervention. The models perform self-supervised learning from the extracted personality, social style, and demographic features, eliminating the need for manual feature engineering and offer creation. This achieves high automation levels while managing training complexity through automated workflows.
Solution Approach 2:
The patent employs parameter optimization techniques that automatically adjust model parameters during training based on performance feedback. By implementing automated hyperparameter tuning and model selection, the system reduces the complexity of initial setup and training while achieving high automation. The models adapt their parameters automatically based on the extracted features and performance metrics, minimizing manual intervention.
Data Source
AI summary
In some examples, personality trait-based customer behavior prediction may include extracting personality features from images associated with a user. Social style features may be extracted from social data associated with the user. Consumer demographics features may be extracted from consumer demographics data associated with the user. Based on a probability analysis of the extracted features, relevant features may be selected from the images, the social data, and the consumer demographics data. Historical purchase features may be extracted from historical purchase data associated with the user. At least one machine learning model may be trained based on the extracted features, and used to generate a next best offer for the user for purchase of a product or a service. A purchase of the product or the service may be performed based on the generated offer.