Word Vector Recommendations for Context-Aware Personalization
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Solution Overview
Problem
Existing recommendation systems fail to provide personalized recommendations based on user interactions and contextual relatedness of words, leading to non-personalized user experiences.
Innovation Solution
A computer-implemented system constructs word vectors between pairs of words using a construction algorithm, assigns scores based on semantic and edit distance contextual relatedness, and maps user interactions to generate personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If general advertisement or simple query-based methods are used for recommendations, then the system is simple and easy to implement, but the personalization and relevance of recommendations deteriorates
Solution Approach 1:
The patent transforms the recommendation system from simple keyword matching to a sophisticated semantic analysis system by changing the parameter of word representation from discrete tokens to continuous vector embeddings. This allows the system to capture nuanced semantic relationships and user preferences, significantly improving personalization capability while maintaining a relatively simple overall architecture.
Solution Approach 2:
The patent introduces word vectors as an intermediary representation layer between user interactions and recommendation generation. These vectors serve as mediators that encode semantic meaning, allowing the system to understand contextual relationships without requiring complex rule-based logic, thus improving personalization without proportionally increasing system complexity.
2Speed
If traditional recommendation systems without semantic analysis are used, then the processing speed is fast, but the measurement precision of user intent and contextual relatedness deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing word vectors in a lookup table before the actual recommendation query. When a user interaction occurs, the system can quickly retrieve relevant vectors without performing complex calculations in real-time, thus maintaining fast processing speed while achieving high semantic understanding accuracy through the pre-prepared vector representations.
Data Source
AI summary
In various example embodiments, a system and method for constructing and scoring word vectors between natural language words and generating output to a user in the form of personalized recommendations are presented.