Personality-Based Product Recommendation Matrix
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Solution Overview
Problem
Conventional recommender systems fail to accurately recommend products when the purchase is between two individuals, as they do not consider the relationship between the sender and recipient, and may result in similar gifts being received from multiple senders due to lack of consideration for the type and strength of their connection.
Innovation Solution
A system that determines personality traits of both the sender and recipient using a five-factor model, calculates personality-product scores, and creates a multidimensional collaborative matrix to recommend products based on their affinity scores, incorporating business strategies and product psychographic portfolios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional recommender systems use collaborative filtering or content-based filtering, then product recommendations can be generated, but the system fails to account for interpersonal relationships between sender and recipient, resulting in generic recommendations that do not capture the nuances of gifting contexts
Solution Approach 1:
The patent introduces a new dimension of analysis by incorporating interpersonal relationship metrics between sender and recipient. The system creates a multidimensional collaborative matrix that includes not only user-product interactions but also relationship strength, relationship type, and personality trait compatibility. This dimensional expansion allows the system to move beyond traditional collaborative filtering limitations and capture the nuanced context of gifting scenarios.
Solution Approach 2:
The patent introduces personality traits as an intermediary element that mediates between user behavior data and product recommendations. By applying the five-factor model to extract personality characteristics of both sender and recipient, the system creates a bridging layer that translates raw behavioral data into meaningful relationship context. This intermediary personality profile enables the system to infer relationship dynamics and make more accurate recommendations.
2Measurement precision
If the system incorporates personality traits and multidimensional collaborative matrices, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex recommendation problem into distinct modular components: (1) personality trait extraction module using the five-factor model, (2) relationship strength calculation module, (3) personality-product score association module, and (4) multidimensional collaborative matrix construction module. Each module handles a specific aspect of the analysis independently, making the overall system more manageable and maintainable despite the increased complexity.
Solution Approach 2:
The patent creates a universal personality profile framework that serves multiple functions within the recommendation system. The same personality traits extracted through the five-factor model are used for: (1) understanding the sender's gifting preferences, (2) understanding the recipient's product preferences, (3) calculating relationship compatibility, and (4) generating personalized recommendations. This multi-functionality reduces redundancy and manages complexity by reusing the same data structure across different analytical tasks.
Data Source
AI summary
Apparatuses, methods, and non-transitory computer readable medium that provide recommendations include determining personality traits of a sender and a recipient by applying a five-factor model to a plurality of datasets. Further, the method comprises associating a personality-product score with each of a plurality of products based on the personality traits and performing a need analysis on the user data to determine desired products from amongst the plurality of products. Further, the method comprises determining a multidimensional collaborative matrix by aggregating the personality traits, the personality-product score, the desired products, and product psychographic portfolio. Further, the method comprises determining an affinity score for at least one of the sender and the recipient towards each of the plurality of products based on the multidimensional collaborative matrix and recommending at least one product from amongst the plurality of products to the sender based on the affinity score.