Graph-Based Recommendation System Pipeline
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Solution Overview
Problem
Current recommendation systems require advanced technical skills to implement and often rely on a single algorithm, failing to provide personalized and situational recommendations effectively across diverse applications.
Innovation Solution
A graph-based recommendation system framework that allows users to design and manage recommender systems using a data model agnostic tool, enabling the creation of a pipeline with multiple phases defined in a declarative language, connecting to various graph databases and employing hybrid scoring methods and machine learning algorithms for real-time recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single algorithm is used to determine recommendations, then the system is simple to implement, but it cannot meet diverse recommendation needs across different applications
Solution Approach 1:
The recommendation system is divided into multiple independent algorithms (collaborative filtering, content-based filtering, hybrid approaches) that can be selectively applied to different data types and scenarios. Each algorithm operates as a separate module that processes specific aspects of recommendation generation, allowing the system to handle diverse needs without requiring complete system redesign.
Solution Approach 2:
The system implements a universal framework that can execute multiple different recommendation algorithms through a common interface. This multi-functional architecture allows the same system to serve various application domains (ecommerce, social networks, travel, entertainment, financial services, logistics, manufacturing, marketing, customer success and human resources) by simply configuring which algorithms to apply, rather than building separate systems for each domain.
2Measurement precision
If advanced algorithms and multiple methods are employed, then recommendation accuracy improves, but the system requires developers with advanced skills to implement
Solution Approach 1:
The system introduces an intermediary layer (the unified recommendation framework) that sits between the complex algorithms and the end users. This framework handles the complexity of algorithm selection, parameter tuning, and result aggregation automatically, allowing users to access advanced recommendation capabilities without needing to understand the underlying complex algorithms. The framework translates simple user requirements into appropriate algorithmic processing.
3Adaptability or versatility
If multiple algorithms are integrated, then personalized and situational recommendations improve, but the computational resources and processing time increase
Solution Approach 1:
The system dynamically selects and adjusts which algorithms to apply based on the specific recommendation scenario, data availability, and performance requirements. Rather than always running all algorithms, the system adapts its computational approach by activating only the necessary algorithms for each situation, thereby maintaining personalization capabilities while optimizing processing efficiency for different operational contexts.
Data Source
AI summary
Techniques are disclosed to provide a graph based recommendation system. A recommendation engine definition that includes for each of a plurality of pipeline phases a corresponding phase definition and data indicating a location of the phase in a pipeline defined by the recommendation engine definition is stored in a memory of other storage device. The recommendation engine definition is used to generate programmatically one or more procedures to provide a recommendation engine that implements the pipeline. An API usable by a client to obtain a recommendation from the recommendation engine is generated programmatically and exposed.