Trust-Based Embeddings for Recommendation Accuracy
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Solution Overview
Problem
Conventional recommendation systems face challenges in providing accurate recommendations when insufficient data is available, leading to issues like 'data sparsity' or 'cold start', especially for new users or items.
Innovation Solution
The proposed solution involves an electronic device that generates trust-based embeddings by determining correlation information across multiple domains using a Graph Attention Network (GAT) model. These embeddings are then used to update user embeddings and improve recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation models are used that rely on user consumption data, then recommendation accuracy improves with more data, but the system fails when data is insufficient (data sparsity or cold start problems)
Solution Approach 1:
The patent introduces trust relationships as an intermediary mechanism to bridge the gap between users with insufficient data and the recommendation system. By determining trust scores between users and propagating these trust relationships across multiple domains, the system can generate recommendations for users with limited consumption data by leveraging trusted users' preferences in other domains, thus resolving the data sparsity and cold start problems
2Reliability
If the recommendation system collects and processes large amounts of user consumption data over time, then recommendation relevancy improves, but the system cannot provide accurate recommendations for new users or items without sufficient historical data
Solution Approach 1:
The patent implements preliminary action by pre-determining trust relationships between users and establishing trust propagation mechanisms across multiple domains before actual recommendation needs arise. This allows the system to immediately provide accurate recommendations for new users or items by leveraging pre-established trust networks, eliminating the waiting period required to accumulate sufficient historical consumption data
3Adaptability or versatility
If the system uses multiple domains to generate trust-based embeddings, then the ability to handle data sparsity improves, but the system complexity increases
Solution Approach 1:
The patent applies universality by creating a multi-domain trust propagation framework where the same trust relationship mechanisms and embedding generation processes are universally applied across multiple domains. This allows the system to handle data sparsity in any domain by leveraging trust relationships established in other domains, achieving high adaptability while maintaining consistent system architecture that manages complexity through reuse of core components
Data Source
AI summary
An electronic device for generation of recommendations using trust-based embeddings is provided. The electronic device determines first correlation information of a first set of users associated with a first domain. The electronic device generates, based on the first correlation information, a first vector indicating a trust embedding of a first user with respect to the first set of users, in the first domain. The electronic device receives second correlation information associated with a second set of users associated with a second domain. The electronic device utilizes a Graph Attention Network model for the second correlation information to generate a second vector indicative of a trust embedding of the first user, with respect to the second set of users, in the second domain. The electronic device applies a recommendation model on the first vector and the second vector to recommend one or more items to the first user.