Online Adaptive Cross-Domain Recommender Model Using User Feedback
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Solution Overview
Problem
Existing recommender systems face challenges such as data sparsity, cold-start problems, and alignment of different data types across multiple services, particularly in cross-domain scenarios, lacking real-time adaptability and effective integration of user feedback.
Innovation Solution
A cross-domain recommendation model utilizing reinforcement learning for online adaptive training, integrating both explicit and implicit feedback to personalize recommendations and adapt to user behavior changes, employing offline and online training methods to enhance model precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single-domain recommender system is used, then the system is simple to implement, but it suffers from data sparsity and cold-start problems when serving multiple services
Solution Approach 1:
The patent implements a cross-domain recommender system where a single model serves multiple services (e.g., e-commerce, entertainment, education) by learning shared user preferences and item representations across different domains. This universal model addresses data sparsity in individual domains by leveraging information from other domains, thereby maintaining recommendation quality without requiring separate models for each service.
Solution Approach 2:
The patent merges multiple domain-specific recommendation tasks into a unified cross-domain recommendation framework. By combining user-item interactions from different domains and training a joint model, the system consolidates data across services to overcome data sparsity issues that would plague individual single-domain models.
2Device complexity
If offline training is used, then the model structure can be pre-determined, but the system lacks real-time adaptability to user behavior changes
Solution Approach 1:
The patent implements a dual training paradigm where the model undergoes offline pre-training to establish its basic structure and parameters, followed by online continuous training to dynamically adapt to changing user preferences and behaviors. This dynamic approach allows the system to maintain a manageable model structure while continuously updating its parameters in real-time based on new user interactions.
Solution Approach 2:
The patent performs preliminary offline training to pre-establish the model's architecture, feature representations, and initial parameters before deployment. This preliminary action prepares the model structure in advance, reducing the computational complexity of real-time online updates while ensuring the model is ready to adapt quickly to new data streams.
3Reliability
If cross-domain recommendation is implemented, then data sparsity is reduced by leveraging multiple domains, but the system complexity increases
Solution Approach 1:
The patent designs a universal cross-domain recommendation model that handles multiple services and domains through a unified architecture. This universal model reduces system complexity compared to maintaining separate models for each domain, while simultaneously improving recommendation quality by leveraging data from all domains to address sparsity issues.
Solution Approach 2:
The patent segments the cross-domain recommendation problem into manageable components including domain-specific feature extraction modules, a shared user preference modeling layer, and domain-agnostic recommendation generation. This segmentation allows the system to handle complexity in an organized manner while maintaining the benefits of cross-domain learning.
4Adaptability or versatility
If online learning is adopted, then the system can adapt to evolving user preferences, but training frequency and computational resources increase
Solution Approach 1:
The patent implements periodic online training where the model is retrained at scheduled intervals or triggered by specific events (e.g., significant data accumulation, performance degradation thresholds). This periodic approach balances the need for adaptability with computational efficiency, avoiding continuous training while ensuring the model remains up-to-date with evolving user preferences.
Solution Approach 2:
The patent employs partial online training by updating only specific model components or parameters that are most sensitive to user preference changes, rather than retraining the entire model. This selective updating approach reduces computational overhead while maintaining the adaptability benefits of online learning.
Data Source
AI summary
A method, performed by an electronic device, for a Recommender System that can adapt to various domains is provided. The method may include providing, by the electronic device, a recommendation to a user based on a profile of the user, receiving, by the electronic device, feedback based on the recommendation, and providing, by the electronic device, an updated recommendation based on the received feedback, wherein the feedback is at least one of explicit or implicit.