Personalized Face Perception Model for Dating Profile Screening
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Solution Overview
Problem
Current online dating services and mobile dating applications face challenges in providing highly personalized recommendations, as users struggle to sift through numerous profiles, leading to a time-consuming and frustrating experience, as existing systems rely on general algorithms rather than individual preferences, resulting in lower compatibility rates.
Innovation Solution
A deep learning-based face perception engine is developed, utilizing a convolutional neural network to create a personalized face perception model for each individual by training on their specific set of training images and desirability scores, allowing the model to infer desirability scores for new face images with high accuracy, thus reducing the need for manual screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually screen through a large number of profile photos to find potential dates, then they can make personalized decisions based on their preferences, but the process becomes time-consuming and frustrating
Solution Approach 1:
The system creates a digital copy of the user's decision-making process by training a deep learning model on the user's labeled profile photos. The model learns to replicate the user's preferences and makes automated decisions that mirror their personal choices, eliminating the need for manual screening while preserving personalization accuracy
Solution Approach 2:
The system enables the user's own preferences to serve the screening function automatically. By having the user label a small set of training photos and then using that data to train an automated model, the system allows the user's personal criteria to continuously evaluate new profiles without requiring their ongoing time investment
2Reliability
If existing dating services use general algorithms for recommendations, then the system complexity remains low, but the compatibility rates and user satisfaction decrease
Solution Approach 1:
The system segments the recommendation problem into two distinct components: a training phase where the user provides labeled examples of their preferences, and an inference phase where the trained model automatically evaluates new profiles. This segmentation allows the system to achieve high compatibility rates through personalized models while managing complexity by separating the data collection burden from the ongoing recommendation process
Solution Approach 2:
The system performs preliminary action by having the user label a small set of training photos in advance. This preliminary labeling effort creates a personalized dataset that enables the deep learning model to be trained once, after which the model can automatically and consistently apply the user's preferences to evaluate numerous new profiles without requiring further user input
Data Source
AI summary
Various embodiments of a deep learning (DL)-based face perception engine for constructing, providing, and applying a highly-personalized face perception model for an individual through a deep learning process are disclosed. In some embodiments, a disclosed face perception engine includes a deep neural network configured for training a personalized face perception model for a unique individual based on a standard set of training images and a corresponding set of decisions on the set of training images provided by the unique individual. When sufficiently trained using the standard set of training images and the corresponding set of decisions, the personalized face perception model for the unique individual perceives a new face photo/image as if through the eyes of that unique individual. Hence, the trained face perception model can be used an “agent” or “representative” of the associated person in making very personal decisions, such as to decide if a given face photo/image includes a desirable face in the eyes of that person.