Tag Recommendation Model Using Semantic Enhanced Representation and Social Network Aggregation
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Solution Overview
Problem
Existing methods for obtaining user interest tags face challenges such as the cold start problem, insufficient characteristic expression, and overfitting due to reliance on conventional models that fail to capture deep semantic information and incorporate social networks effectively.
Innovation Solution
A method involving the collection of training materials, representation using a semantic enhanced representation frame like ERNIE, aggregation of social networks to enhance semantic vectors, and training a double-layer neural network to generate accurate interest tags, addressing the limitations of rule-based and conventional model-based approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional models are used for interest tag prediction, then the model structure is simple and easy to implement, but the accuracy is insufficient and the model cannot capture deep semantic information
Solution Approach 1:
The patent employs a nested neural network structure where an inner neural network processes semantic vectors generated by an outer semantic enhanced representation frame. This nested architecture allows the model to capture deep semantic information through multiple layers of processing, significantly improving interest tag prediction accuracy while managing complexity through hierarchical organization.
Solution Approach 2:
The patent introduces a semantic enhanced representation frame that transforms traditional one-dimensional tag prediction into a multi-dimensional semantic space. By incorporating social network aggregation and deep semantic representation, the model operates in higher dimensions to capture complex user interest patterns, thereby improving prediction accuracy beyond what conventional two-dimensional models can achieve.
2Measurement precision
If rule-based methods are used for obtaining interest tags, then the implementation is simple and fast, but the characteristic expression is insufficient and cannot capture user interests accurately
Solution Approach 1:
The patent replaces rule-based mechanical processing with a neural network-based semantic processing system. Instead of using fixed rules to match keywords with tags, the model uses semantic enhanced representation frames and neural networks to understand and infer user interests from textual data, achieving more accurate and flexible interest tag prediction.
Solution Approach 2:
The patent changes the fundamental parameters of interest tag prediction by moving from discrete rule-based matching to continuous semantic vector representations. The model uses semantic enhanced representation frames that transform textual features into continuous vectors, enabling more nuanced and accurate interest capture compared to traditional discrete parameter approaches.
3Adaptability or versatility
If conventional models are used, then the training process is simple and fast, but overfitting occurs and the model lacks generalization capability
Solution Approach 1:
The patent performs preliminary action by pre-training the semantic enhanced representation frame and neural network models using extensive training data and social network aggregation before actual inference. This pre-training phase establishes robust feature representations and relationships that improve generalization capability during subsequent application, reducing the need for extensive retraining on new data.
Solution Approach 2:
The patent introduces social network aggregation as an intermediary component between user data and interest tag prediction. This intermediary layer processes and aggregates social network information to create enriched semantic vectors, which then feed into the neural network for tag prediction. This intermediary structure improves generalization by providing a more comprehensive view of user interests through social context.
4Adaptability or versatility
If the model does not incorporate social networks, then the structure is simple, but the cold start problem occurs and the model cannot accurately predict interests of new users
Solution Approach 1:
The patent makes the social network aggregation component universal by designing it to handle multiple scenarios: it can process existing user data, infer interests for new users through social network connections, and adapt to different user profiles. This multi-functional approach enables the model to address cold start problems for new users while maintaining effectiveness for existing users, thereby improving overall adaptability.
Data Source
AI summary
The disclosure provides a method for training a tag recommendation model. The method includes: collecting training materials that comprise interest tags in response to receiving an instruction for collecting training materials; obtaining training semantic vectors that comprise the interest tags by representing features of the training materials using a semantic enhanced representation frame; obtaining training encoding vectors by aggregating social networks into the training semantic vectors; and obtaining a tag recommendation model by training a double-layer neural network structure using the training encoding vectors as inputs and the interest tags as outputs. Therefore, the interest tags obtained in the disclosure are more accurate.