Nonsymmetric Determinantal Point Process for Streaming Compatibility
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for identifying compatible sets of entities in large datasets are inefficient due to high memory requirements and inability to handle streaming data, as they often require multiple passes over the entire dataset and are not suitable for online settings.
Innovation Solution
A learning system that maintains a compatibility distribution using a stream of compatible sets to update components matrices with embeddings, allowing for real-time inference and recommendation of compatible sets with sub-linear memory usage and single-pass processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques are used to identify compatible sets of entities in large datasets, then compatibility accuracy can be maintained, but memory requirements become excessively high and multiple passes over the entire dataset are required
Solution Approach 1:
The patent segments the large dataset into streaming data chunks that are processed sequentially. Instead of loading the entire dataset into memory, the system processes data in a single pass through the stream, maintaining only the necessary compatibility distribution matrices in memory. This segmentation approach allows the system to handle large datasets with limited memory resources while preserving compatibility accuracy.
Solution Approach 2:
The patent transforms the problem from requiring full dataset storage to using compact compatibility distribution matrices that capture entity compatibility relationships. By changing the representation parameters from raw data storage to probabilistic compatibility matrices, the system reduces memory requirements from O(n) to O(k) where k is the number of entities in the compatibility model, not the entire dataset size.
2Measurement precision
If existing techniques process entire datasets multiple times to identify compatible sets, then comprehensive compatibility analysis is achieved, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary processing by precomputing compatibility distributions from streaming data before the actual compatibility query is made. The system continuously updates compatibility matrices as data arrives in the stream, so when a compatibility query is needed, the analysis is already prepared or can be completed rapidly using the pre-computed distributions, avoiding multiple passes through the entire dataset.
Solution Approach 2:
The patent implements continuous processing of streaming data to maintain up-to-date compatibility distributions. Instead of periodic batch processing that requires stopping and restarting multiple times, the system continuously updates compatibility information as data flows through the stream, enabling real-time compatibility queries without interrupting the data processing flow.
3Reliability
If traditional methods store entire datasets to maintain compatibility distributions, then accurate compatibility recommendations are provided, but memory utilization increases
Solution Approach 1:
The patent creates a compressed probabilistic representation (compatibility distribution matrix) that copies only the essential compatibility relationships between entities rather than storing complete entity data. This copying approach captures the necessary information for accurate compatibility recommendations while using minimal memory, as the matrix stores only pairwise compatibility probabilities rather than full entity attributes.
Solution Approach 2:
The patent changes the storage parameter from complete entity datasets to compact compatibility probability matrices. By representing compatibility information as probability distributions rather than raw data, the system achieves the same recommendation accuracy with dramatically reduced memory utilization, storing O(k²) compatibility values instead of O(n) entity records where n is the total dataset size.
4Adaptability or versatility
If online learning is implemented to update compatibility distributions in real-time, then adaptability to streaming data is improved, but computational complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where compatibility distributions are continuously updated based on observed entity co-occurrences in the streaming data. The system uses the observed compatibility patterns to refine the probability distributions, creating a feedback loop that improves recommendation accuracy over time while maintaining computational efficiency through incremental updates rather than complete reprocessing.
Solution Approach 2:
The patent applies partial updates to compatibility distributions by only processing and updating the specific entity pairs that appear in the current data stream chunk, rather than recomputing the entire compatibility matrix. This partial action approach reduces computational complexity from O(k²) per update to O(m) per update where m is the number of observed entity pairs in the stream, which is typically much smaller than k².
Data Source
AI summary
Embodiments provide systems, methods, and computer storage media for a Nonsymmetric Determinantal Point Process (NDPPs) for compatible set recommendations in a setting where data representing entities (e.g., items) arrives in a stream. A stream representing compatible sets of entities is received and used to update a latent representation of the entities and a compatibility distribution indicating likelihood of compatibility of subsets of the entities. The probability distribution is accessed in a single sequential pass to predict a compatible complete set of entities that completes an incomplete set of entities. The predicted complete compatible set is provided a recommendation for entities that complete the incomplete set of entities.