Destination Prediction Using Embedding Matrices
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Solution Overview
Problem
Conventional navigational search algorithms rely on user input and recency, proximity, and popularity, failing to provide a context-sensitive search experience that effectively predicts destinations based on user profiles and historic location data.
Innovation Solution
A computer-implemented method using an embedding matrix to generate semantic embeddings for candidate destinations from user and context data, fed into machine learning layers to produce probability scores for personalized destination prediction, with the ability to adapt and improve through user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional navigational search algorithms are used based on user input and recency, proximity, and popularity, then the search system is simple to implement, but it fails to provide context-sensitive destination predictions
Solution Approach 1:
The system performs preliminary encoding of user profiles, historic location data, and context information into embedding vectors before the actual search query is processed. This pre-processing allows the complex contextual analysis to be prepared in advance, enabling context-sensitive predictions without significantly increasing the complexity of the real-time search operation.
Solution Approach 2:
The patent introduces embedding vectors as an intermediary representation layer between the raw input data (user profiles, location history, context) and the destination prediction output. These embedding vectors serve as a mediator that captures contextual relationships in a compressed form, allowing the system to achieve context-sensitivity without directly processing the full complexity of all input variables.
2Measurement precision
If detailed user profiles and historic location data are analyzed for destination prediction, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
User profiles and historic location data are pre-processed and encoded into embedding vectors before actual destination prediction is needed. This preliminary encoding captures the essential patterns in user behavior and location preferences, allowing accurate predictions to be made quickly when a search query is submitted without re-processing the entire historic data set.
Solution Approach 2:
The system transforms the high-dimensional raw data (detailed user profiles and location histories) into lower-dimensional embedding vectors that preserve the essential information. This parameter transformation maintains prediction accuracy by preserving the semantic relationships in the data while reducing the computational burden of processing.
3Adaptability or versatility
If conventional search algorithms are used, then the system requires minimal computational resources, but it cannot provide personalized destination predictions based on user behavior patterns
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and encodes them into embedding vectors during off-peak times or in batches, rather than computing personalized predictions in real-time for each query. This allows the system to provide personalized destination predictions based on learned user patterns without consuming excessive computational resources during actual search operations.
Solution Approach 2:
The patent creates simplified copies of user profiles and location histories in the form of embedding vectors that capture the essential personalization information. These compact representations allow the system to provide personalized predictions with minimal computational overhead, as the complex user behavior analysis has already been performed and stored in compressed form.
Data Source
AI summary
The present disclosure relates to a concept for machine-learning-based prediction of a destination for a user. Based on historic search data associated with the user, at least one candidate destination is determined based on the user and a given context. A plurality of embedding vectors are determined from an embedding matrix, wherein the embedding vectors are associated with the at least one candidate destination, the user, and the given context. The embedding matrix comprising embedding vectors for different components of the historic search data. The plurality of embedding vectors are fed into one or more first neural network layers to generate a semantic embedding for the candidate destination. The semantic embedding is into one or more second neural network layers to generate a probability score for the candidate destination.