Preference Prototype Learning for Cross-Domain Recommendation Accuracy
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Solution Overview
Problem
Conventional cross-domain recommendation systems struggle to accurately capture user preferences due to reliance on specific item features, leading to sub-optimal recommendations and limited generalization across domains, and fail to form an end-to-end structure for effective user preference extraction.
Innovation Solution
A universal cross-domain recommendation method based on preference prototype-aware learning, utilizing a hybrid encoder and decoder to quantify user preferences through prototype-aware learning, capturing deeper level dependencies between items and integrating contrastive learning to adjust interaction spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional cross-domain recommendation systems extract user preferences from specific item features, then they can process data from multiple domains, but they fail to accurately capture user preferences and produce sub-optimal recommendations
Solution Approach 1:
The patent extracts user preference information from interaction patterns rather than item features. By using a transformer-based model that processes user-item interaction sequences, the system extracts preference representations that capture user intentions across domains without being constrained by specific item feature taxonomies, thereby resolving the contradiction between cross-domain versatility and preference capture accuracy
Solution Approach 2:
The patent introduces an intermediary preference representation layer that mediates between item features and user preferences. The transformer model creates a bridging representation that translates item interactions into user preference embeddings, enabling accurate preference extraction across domains while maintaining the ability to process diverse item types without requiring domain-specific feature engineering
2Device complexity
If the system focuses on specific item features to represent user preferences, then it can simplify preference representation, but it introduces noise and limits generalization ability
Solution Approach 1:
The patent implements a universal preference representation framework using transformer models that can process various types of items across different domains through a unified architecture. The model learns domain-agnostic preference representations by processing interaction sequences in a standardized manner, enabling the same system to generalize across e-commerce, entertainment, and other domains without requiring domain-specific feature engineering or model variants
Solution Approach 2:
The patent changes the representation parameters from static item feature vectors to dynamic preference embeddings derived from interaction sequences. By transforming the representation space from item-centric to user-preference-centric, the system eliminates the noise introduced by specific item features while maintaining computational efficiency through the transformer architecture's parameterized attention mechanisms
3Quantity of substance
If conventional methods use masking mechanisms to predict user preferences, then they can work with insufficient data, but the masking mechanism becomes more challenging when data volume is insufficient
Solution Approach 1:
The patent performs preliminary action by pre-processing interaction data into standardized sequences and pre-computing attention weights before the main preference prediction task. The transformer model builds up preference representations through sequential processing of interaction history, allowing the system to effectively utilize limited data by accumulating information across multiple interaction steps rather than relying on masking techniques that struggle with insufficient data volume
Data Source
AI summary
A universal recommendation method based on preference prototype-aware learning, which belongs to the big data analysis technologies. The present disclosure, when realizing the universal cross-domain recommendation, quantitatively learns the user preference through a preference prototype-aware learning method while minimizing interference from the source domain. The method of the present disclosure consists of two complementary components: a hybrid encoder and a preference prototype-aware decoder, which form an end-to-end unified framework suitable for various real-world scenarios. The hybrid encoder uses a hybrid network to learn general representations of interactive items and capture the intrinsic relationships between items across different domains. The preference prototype-aware decoder implements a learnable prototype matching mechanism to quantitatively perceive user preferences and can accurately capture user preferences at a higher semantic level. The preference prototype-aware decoder can also avoid interference caused by item features from the source domain.