Machine Learning Model Segmentation for Booking Probability Prediction
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Solution Overview
Problem
The complexity of numerous features in online marketplace listings makes it difficult for human calculations to determine the dependency of each factor on other factors, leading to stale booking probability outputs and feedback loops during model training, especially when listings use a flat pricing strategy.
Innovation Solution
The use of machine learning methodologies to predict price-dependent features from price and price-independent features, generating synthetic training data for a booking probability model by analyzing listings with single price changes, and training machine learning models with hard and soft layers to learn correlations between listing features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to predict price-dependent features, then booking probability accuracy is improved, but device complexity increases
Solution Approach 1:
The machine learning model is divided into two distinct components: a first model that predicts price-dependent features from price and price-independent features, and a second model that predicts booking probability using the generated features. This segmentation allows each model to specialize in specific tasks, improving overall accuracy while making the system more manageable despite the increased complexity.
2Measurement precision
If synthetic training data is generated through machine learning, then model training accuracy is improved, but loss of information increases
Solution Approach 1:
The system generates synthetic training data by copying and transforming existing data patterns through the first machine learning model. The model learns from actual listings with single price changes and generates synthetic examples that preserve the underlying relationships between price changes, price-independent features, and price-dependent features, thereby maintaining data fidelity while expanding the training dataset.
3Ease of operation
If listings use flat pricing strategy, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The first machine learning model acts as an intermediary that translates simple flat pricing inputs into rich price-dependent features. Even when listings use flat pricing strategies, the model generates realistic variations in price-dependent features based on learned patterns from data, thereby maintaining prediction accuracy without requiring complex manual pricing management.
Data Source
AI summary
Systems and methods are provided for generating a first trained machine learning model, the first machine learning model comprising a plurality of hard layers for learning correlations between listing features and a plurality of soft layers, each soft layer for learning correlations for a prespecified listing feature. The systems and methods further provide for analyzing, using the first trained machine learning model, each of a plurality of price changes and price independent listing features for the first listing to determine a predicted value for each of a prespecified price dependent listing feature for each of the plurality of price changes for the first listing and generating, using the first trained machine learning model, the predicted value for each of the prespecified price dependent listing features for each of the plurality of price changes for the first listing.