Personalized Rating Vector Generation for User Recommendations
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Solution Overview
Problem
Conventional recommendation systems, particularly those using latent factor methods and matrix factorization, fail to capture the influence of multiple factors in user preferences and do not provide personalized recommendations effectively, especially for users in specific emotional or cognitive states.
Innovation Solution
The method involves extended collective matrix factorization with group-sparse embeddings to generate personalized rating vectors by combining user-specific and characteristics-based ratings, using element-wise multiplication of user vectors, relevance weights, and characteristics vectors to provide tailored recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If latent factor methods and matrix factorization are used, then the system can process large numbers of options, but the system fails to capture multiple factors in user preferences and does not provide personalized recommendations effectively
Solution Approach 1:
The patent segments user preferences into multiple independent factor vectors (e.g., content-based factors, collaborative filtering factors, demographic factors) rather than using a single latent factor vector. Each factor vector captures a distinct aspect of user preference, allowing the system to preserve and process multiple factors simultaneously without information loss.
Solution Approach 2:
The patent combines multiple factor vectors into a composite preference representation through element-wise multiplication and aggregation. This composite approach integrates information from different sources (content-based, collaborative, demographic) to create a richer, more personalized user preference model that captures multiple factors simultaneously.
2Measurement precision
If conventional recommendation systems are used, then the system can offer a large selection of options, but the system fails to provide accurate personalized recommendations for users in specific emotional or cognitive states
Solution Approach 1:
The patent applies local quality by tailoring the recommendation process to specific user states (emotional, cognitive, contextual). The system adjusts the weighting and combination of factor vectors based on the user's current state, providing localized, state-specific recommendations rather than a one-size-fits-all approach.
Solution Approach 2:
The patent introduces dynamics by making the recommendation model adaptive to changing user states. The system dynamically adjusts factor weights and combines vectors differently based on real-time user context, allowing the recommendation accuracy to improve as the system responds to specific emotional and cognitive states.
3Loss of information
If collaborative filtering with neighborhood methods is used, then the system can identify user-product associations, but the system does not effectively model multiple influencing factors
Solution Approach 1:
The patent merges collaborative filtering associations with content-based factors and demographic information into a unified multi-factor model. By combining these different information sources through vector multiplication and aggregation, the system preserves user-product associations while effectively modeling multiple influencing factors in an integrated framework.
Data Source
AI summary
A method may include generating a personalized rating vector associated with a querying user. The personalized rating vector may relate personalized ratings to options. The personalized rating vector may be generated via element-wise multiplication of a user vector, a relevance weight, and a characteristics vector. The user vector may relate user ratings to the querying user and to the options. The relevance weight may be associated with a characteristic of the querying user. The characteristics vector may relate characteristics ratings to a user characteristic and to the options. The user characteristic may correspond at least in part to the characteristic of the querying user. The method may further include generating a recommendation for the querying user based at least in part on the personalized rating vector, the recommendation associated with at least one option of the options of the personalized rating vector.