Egocentric Collaborative Filtering via Graph Traversal on Large Datasets
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Solution Overview
Problem
Existing recommendation systems face performance challenges in providing real-time product recommendations due to the need for frequent recomputation of queries on large, dynamically changing datasets, especially when serving multiple users simultaneously, as traditional SQL databases struggle with memory-intensive join operations.
Innovation Solution
Utilizing Hipergraph primitives to perform graph traversals instead of traditional relational database joins, enabling efficient collaborative filtering by generating a graph model from consumer access data and applying Hipergraph operations to traverse vertices and edges, thereby reducing memory requirements and improving computational speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SQL database joins are used for collaborative filtering, then the system can handle complex relational queries, but memory usage and computation time increase significantly
Solution Approach 1:
The patent replaces the mechanical join operations of traditional SQL databases with a probabilistic sampling mechanism. Instead of performing full relational joins that require loading entire tables into memory, the system uses graph traversal with sampling to estimate join results, dramatically reducing memory requirements while maintaining acceptable query accuracy.
Solution Approach 2:
The system changes the parameter of memory allocation by introducing a sampling rate parameter. By adjusting the sampling rate, the system can control the trade-off between memory usage and query accuracy, allowing it to operate within limited memory constraints while still providing meaningful results.
2Measurement precision
If traditional SQL database joins are used for collaborative filtering, then the system can handle complex relational queries, but computation time increases significantly
Solution Approach 1:
The patent replaces computationally intensive join operations with efficient graph traversal algorithms. By representing data as a graph structure and using sampling-based traversal, the system avoids the O(n*m) complexity of traditional joins and achieves linear time complexity relative to the number of edges traversed.
Solution Approach 2:
The system performs partial joins by sampling a subset of records rather than processing complete tables. This partial action approach provides sufficiently accurate results for recommendation purposes while dramatically reducing computation time, as the sampling rate can be tuned to balance speed and accuracy requirements.
3Productivity
If graph traversals are performed on large datasets, then real-time recommendations can be provided, but the system complexity increases
Solution Approach 1:
The patent introduces a graph representation as an intermediary data structure between the raw database and the recommendation engine. This graph intermediary simplifies the traversal operations by pre-computing relationships and storing them in an optimized format, making real-time queries feasible without directly complexifying the underlying database system.
Solution Approach 2:
The system segments the large dataset into a graph structure with vertices and edges, allowing independent traversal of different relationship types. This segmentation enables parallel processing of multiple graph traversal operations and simplifies the management of complex relationships by breaking them into discrete, traversable components.
4Ease of operation
If multiple users are served simultaneously with real-time recommendations, then user satisfaction improves, but the computational load on the server increases
Solution Approach 1:
The system serves multiple users simultaneously by performing partial graph traversals for each user query. Instead of computing complete recommendation sets for all users, it samples sufficient paths to provide personalized recommendations, reducing the computational load per user while maintaining overall system throughput and user satisfaction.
Solution Approach 2:
The system performs preliminary graph construction and indexing during off-peak periods, pre-computing relationship structures that can be quickly traversed during user queries. This preliminary action reduces the computational load during peak usage by moving heavy lifting to background processing.
Data Source
AI summary
One embodiment of the present invention provides a system for generating a product recommendation. During operation, the system obtains data indicating vertices and edges of a graph. The vertices represent consumers and products and an edge represents an access relationship. The system may receive a query indicating an ego for determining a product recommendation. The system may then traverse the graph from a vertex representing the ego through a plurality of edges to a plurality of vertices representing products. The system may traverse the graph from the plurality of vertices representing products to a plurality of vertices representing other consumers. The system may then traverse the graph from the plurality of vertices representing other consumers to a plurality of vertices representing other products. The system may generate a recommendation that based on the plurality of vertices representing other products.