Egocentric Collaborative Filtering via Graph Traversal on Large Datasets
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Solution Overview
Problem
Existing recommendation systems face performance challenges in real-time collaborative filtering on large datasets due to the need for frequent recomputation of queries and high memory requirements when handling simultaneous user interactions, especially when using traditional SQL databases.
Innovation Solution
Utilizing Hipergraph primitives to perform graph traversals instead of 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 computation speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional SQL databases are used for collaborative filtering, then data storage and querying capability is provided, but computation time and memory requirements increase significantly for large datasets
Solution Approach 1:
The patent replaces traditional relational database join operations with graph traversal operations using Hipergraph. Instead of using SQL joins to find connected entities, the system represents data as a graph structure and performs traversals through edges, which are computationally more efficient for this type of query pattern. This substitution of mechanical operations directly addresses the performance issues with SQL databases on large datasets.
Solution Approach 2:
The patent segments the collaborative filtering computation into distinct graph traversal phases: first traversing from users to items, then from items to other users, and finally from those users to recommended items. This segmentation allows each phase to be optimized independently and enables parallel processing, significantly reducing overall computation time and memory requirements compared to monolithic SQL queries.
2Speed
If real-time recommendations are provided for multiple simultaneous users, then service responsiveness is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent performs preliminary graph construction and indexing of user-item relationships before receiving recommendation queries. By pre-processing data into a graph structure with optimized edge representations, the system can quickly traverse and generate recommendations without performing complex computations during the actual query response time. This preliminary action enables real-time responses even under high concurrency.
Solution Approach 2:
The patent implements a dynamic recommendation system that adapts to changing user behaviors and preferences in real-time. The graph structure allows for efficient updates when new user-item interactions occur, and the system can dynamically adjust traversal depths and filtering criteria based on current conditions. This dynamic capability maintains responsiveness while managing system complexity through flexible, condition-based 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.