Neural Set Operations for User Preference Representation
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Solution Overview
Problem
Recommender systems face challenges in accurately learning user preferences due to sparsity issues and limited expressiveness, as they often focus on positive feedback or ignore negative feedback, leading to performance penalties.
Innovation Solution
A method and apparatus that utilize neural set operations to combine both positive and negative feedback items with global preference information, employing a neural network with ensemble operations and multi-layer perceptrons to learn comprehensive user preference representations, ensuring that both types of feedback are integrated into the recommendation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based CF methods (Matrix Factorization) are used to learn user preferences as real-valued vectors, then the sparsity problem is overcome, but the expressiveness of the learned user preference representations is limited due to linear mapping assumptions
Solution Approach 1:
The patent replaces the linear mapping mechanism of traditional Matrix Factorization with a neural network-based non-linear mapping system. The neural network with multiple layers and non-linear activation functions enables complex transformations from original representation space to latent space, significantly improving the expressiveness of user preference representations while maintaining the ability to handle sparse data.
Solution Approach 2:
The patent combines multiple components including neural network layers, non-linear activation functions, and latent space representations to create a composite representation system. This composite approach integrates the strengths of deep learning with collaborative filtering, achieving both sparsity handling and high expressiveness through the synergistic combination of these elements.
2Device complexity
If deep learning-based recommendation systems focus only on positive feedback or directly compress preferences into fixed-length embeddings, then the model complexity is reduced, but the amount of information in user preference representation is limited, resulting in performance penalty
Solution Approach 1:
The patent segments user preference representation into distinct positive feedback representations and negative feedback representations, processing them separately through the neural network. This segmentation allows each type of feedback to be encoded with appropriate attention, preserving more information without requiring excessive model complexity. The separate processing paths enable nuanced representation of different feedback types.
Solution Approach 2:
The patent transforms the fixed-length embedding constraint by introducing a hierarchical representation structure with multiple layers. The neural network progressively transforms input data through intermediate representations, effectively adding dimensional depth rather than relying solely on width. This multi-dimensional transformation preserves information while maintaining computational feasibility.
Data Source
AI summary
The present invention discloses a method and apparatus for recommending user preferences based on neural set operations, wherein on the basis of obtaining a positive feedback interaction sequence and a negative feedback interaction sequence and user global preference information, a sequence of positive feedback interaction vectors and a sequence of negative feedback interaction vectors corresponding to the positive feedback interaction sequence and the negative feedback interaction sequence are simultaneously combined, and the positive feedback preference representation and the negative feedback preference representation are obtained using the ensemble operation. After that, the comprehensive user preference representation is obtained by feeding an MLP with the concatenation of the positive, negative, and global user preference embeddings, and finally the recommendation score is evaluated by calculating the similarity between the comprehensive user preference representation and the vector corresponding to the candidate interaction item, and the user preference recommendation is realized based on the recommendation score, which can improve the accuracy.