Transformer Model Social Network Recommendations
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Solution Overview
Problem
Conventional recommendation models face challenges in providing accurate recommendations when insufficient data is available, leading to issues like 'data sparsity' and 'cold start' scenarios, where they struggle to determine user preferences and item correlations effectively.
Innovation Solution
An electronic device and method utilizing a transformer model that generates recommendations based on social network information, history information, and similarity information, leveraging the connections and interests of users on social media platforms to correlate users and items, even with sparse or incomplete user-item history data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional recommendation models are used, then they can provide recommendations based on user-item history data, but they fail to provide accurate recommendations when data is insufficient (data sparsity and cold start scenarios)
Solution Approach 1:
The patent introduces social network information as an intermediary factor to bridge the gap between users and items. When direct user-item history data is insufficient, the system uses social network connections and user similarities as mediators to infer preferences and generate recommendations, thereby resolving the data sparsity problem.
Solution Approach 2:
The patent extends the recommendation approach from a single dimension (user-item history) to multiple dimensions by incorporating social network structure, user similarities, and collaborative filtering signals. This multi-dimensional approach enables accurate recommendations even when traditional single-dimension data is scarce.
2Measurement precision
If recommendation models rely on sufficient training data, then they can achieve high accuracy, but they cannot operate effectively in data-scarce environments
Solution Approach 1:
The system enables self-service recommendation by leveraging existing social network information and user profile data to generate recommendations without requiring extensive external training data. The model uses available information about user connections and similarities to autonomously infer preferences and provide accurate recommendations in data-scarce conditions.
Data Source
AI summary
Provided is an electronic device for social network information-based recommendation using transformer model. The electronic device receives first history information associated with a set of users for an item of a set of items and determines first similarity information associated with each user with respect to remaining users of the set of users. Further, the electronic device receives social network information associated each user with respect to remaining users of the set of users. The electronic device determines first embedding associated with each user for the item, based on the first history information, the first similarity information, and the social network information. A first transformer model is applied on the first embedding to determine at least user from set of users for the item. First recommendation information including the determined at least one users for the item is rendered.