Deep Learning Recommendation Model Segmentation for Cold-Start Accuracy
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Solution Overview
Problem
Current recommendation systems in e-commerce and online sharing services rely heavily on content-based and collaborative filtering methods, which may not accurately predict user preferences, especially for new users or items with limited historical data.
Innovation Solution
A deep learning-based recommendation method using an integrated model with multiple sub-models to obtain operating probabilities for target users and items, leveraging historical operation information to recommend items by combining probabilities from different sub-models, thus enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If collaborative filtering methods are used for recommendation, then recommendation performance is improved, but accuracy for new users or items with limited historical data deteriorates
Solution Approach 1:
The recommendation model is segmented into multiple sub-models, each specialized in processing different types of data distributions. This segmentation allows the system to handle cold-start problems for new users and items by routing them to appropriate sub-models that can make accurate predictions even with limited historical data, while maintaining overall high recommendation performance through the ensemble of specialized sub-models.
Solution Approach 2:
The system dynamically adjusts parameters including data distribution identification and model selection based on the characteristics of input data. By changing parameters such as selecting which sub-model to use based on data distribution type, the system adapts to different scenarios (new users, new items, or established users/items) to optimize prediction accuracy across all cases while maintaining high overall performance.
2Measurement precision
If multiple sub-models are integrated to improve prediction accuracy, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
An intermediary data distribution identification module is introduced to manage the complexity of multiple sub-models. This intermediary component automatically determines which sub-model to use based on the characteristics of the input data, thereby simplifying the overall system operation. The intermediary handles the complexity of model selection and integration, allowing the system to achieve high prediction accuracy through multiple sub-models without requiring complex manual management of the ensemble.
Data Source
AI summary
Methods and apparatuses for deep learning-based recommendation, electronic devices, and media include: respectively obtaining related information of a target user and related information of a target item; respectively using at least two sub-models in an integrated model to obtain, based on the related information of the target user and the related information of the target item, operating probabilities corresponding to the at least two sub-models; obtaining, based on the operating probabilities corresponding to the at least two sub-models, a target probability about the target user operating the target item; and recommending the target item to the target user based on the target probability.