Machine Learning Model Usage Compensation for Cold-Item Prediction
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Solution Overview
Problem
Existing systems struggle to effectively predict associations between users and items, particularly for 'cold items' lacking sufficient usage data, leading to the 'cold-start' problem where the ranking and prioritization of such items are unclear compared to 'warm items' with ample data.
Innovation Solution
A machine learning model is trained using labeled data that includes content data and selectively uses either actual usage data or simulated usage data based on a random variable, simulating usage data for items lacking sufficient data to compensate for the lack of real-world interactions, thereby enhancing the model's ability to predict associations for both warm and cold items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual usage data is used for training, then prediction accuracy for warm items is improved, but cold items lacking usage data cannot be effectively predicted
Solution Approach 1:
The patent creates simulated usage data copies for cold items by generating synthetic interaction patterns based on content features and item attributes. This allows the model to train on cold items as if they had actual usage data, enabling predictions for items without real-world interactions while maintaining prediction accuracy through the simulated data structure
Solution Approach 2:
The patent performs preliminary data preparation by simulating usage data for cold items before the actual training process. This advance preparation ensures that cold items are not excluded from training, allowing the model to learn associations for all items including those without historical interactions
2Reliability
If only items with sufficient usage data are used for training, then model training quality is improved, but cold items are excluded from the training process
Solution Approach 1:
The patent generates simulated usage data copies for cold items, enabling them to be included in the training set without compromising data quality. This copying approach allows expansion of the training set to include all items while maintaining reliable training signals through synthetically generated interaction patterns
Solution Approach 2:
The patent changes the data availability parameter for cold items by introducing simulated usage data. This parameter transformation allows cold items to transition from a state of no usable data to a state where they can participate in training, thereby increasing the quantity of trainable items while preserving training quality
3Reliability
If the model prioritizes items with more usage data, then prediction reliability for warm items is improved, but cold items remain deprioritized and difficult to recommend
Solution Approach 1:
The patent creates simulated usage data copies for cold items that enable them to be ranked and prioritized alongside warm items. This copying mechanism provides cold items with the necessary training signals to achieve meaningful prediction scores, allowing them to be operationally integrated into recommendation rankings rather than remaining excluded or deprioritized
4Adaptability or versatility
If simulated usage data is generated for all items, then cold items can be effectively trained, but training data quality may be compromised
Solution Approach 1:
The patent applies simulated usage data selectively and locally to cold items that lack sufficient actual usage data, rather than uniformly applying it to all items. This localized approach ensures that warm items with sufficient real data continue to use high-quality actual usage data for training, while cold items receive simulated data only where necessary, thereby maintaining overall training data quality while achieving versatility
Data Source
AI summary
A method of training a machine learning model is provided. The method includes receiving labeled training data in the machine learning model, the received labeled training data including content data for items accessible to a user and input usage data representing recorded interaction between the user and the items, wherein the received content data for each item includes data representing intrinsic attributes of the item. The method further includes selecting a set of the input usage data that excludes input usage data for a proper subset of the items and training the machine learning model based on both the content data and the selected set of input usage data of the received labeled training data for the items.