Node Graph Pruning for Content Recommendation
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Solution Overview
Problem
Content discovery applications face challenges in providing high-quality, relevant recommendations quickly and efficiently, especially when dealing with large volumes of human-categorized content and inconsistent data, which can lead to increased computing resources and user dissatisfaction.
Innovation Solution
The use of node graph pruning techniques, such as diversity pruning and edge pruning, to construct a node graph that focuses on topically similar representations and collections, thereby improving the quality and speed of content recommendations while reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the complexity of recommendation algorithms is increased to improve recommendation quality, then the relevance of recommendations is improved, but the time and computing resources needed to make recommendations increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing node graphs that represent relationships between content items before they are needed for recommendations. The node graph is constructed in advance with edges representing similarity relationships, allowing the recommendation system to quickly traverse pre-computed structures rather than calculating relationships in real-time when a user requests recommendations.
Solution Approach 2:
The system extracts and removes inconsistent or erroneously categorized data from the node graph construction process. By identifying and excluding data that does not meet quality thresholds, the system reduces the computational burden of processing all data while maintaining recommendation quality. This extraction of problematic data prevents it from degrading recommendation relevance while reducing overall processing requirements.
2Reliability
If the complexity of recommendation algorithms is increased to reduce the likelihood of providing irrelevant recommendations, then the relevance of recommendations is improved, but the computing resources (processing and memory) required increase
Solution Approach 1:
The system applies local quality by constructing node graphs with varying levels of detail and connectivity based on local data characteristics. Different portions of the node graph can have different edge densities and relationship types tailored to the specific content domains they represent. This allows the system to concentrate computational resources on high-value relationships while using simpler representations for less critical content areas.
Solution Approach 2:
The system changes parameters such as edge threshold values, node selection criteria, and graph traversal depths to optimize the balance between recommendation quality and resource consumption. By adjusting these parameters based on data quality assessments and performance metrics, the system can dynamically adapt the complexity of the node graph structure to match the available computing resources while maintaining effective recommendation capability.
3Adaptability or versatility
If inconsistent or erroneously categorized data is included in the node graph, then the coverage of content recommendations is improved, but the quality and relevance of recommendations deteriorate
Solution Approach 1:
The system converts the potential harm of inconsistent data into a benefit by using data quality assessment to identify and categorize different types of data relationships. Rather than simply excluding all borderline cases, the system analyzes patterns in potentially inconsistent data and uses this information to improve the node graph structure, such as by creating separate relationship types or applying different traversal rules for different data quality levels.
Solution Approach 2:
The system segments the node graph into different regions or layers based on data quality and consistency. High-quality, consistently categorized data forms the core structure with strong relationship edges, while data with lower consistency or higher uncertainty forms peripheral regions with weaker or more conditional relationships. This segmentation allows the system to maintain broad content coverage while ensuring that recommendation quality is driven by the most reliable data segments.
Data Source
AI summary
This disclosure describes systems and methods that facilitate the generation of recommendations by traversing a graph. Walks that traverse the graph may be initiated from a plurality of different nodes in the node graph. In order to give greater or lesser weight to particular nodes, the walks may have different lengths depending on the nodes from which they are initiated, or an unequal amount of walks may be distributed between nodes from which walks are initiated. A plurality of walks through a node graph may be tracked, and visit counts or scores for nodes in the node graph may be determined. For example, scores may be increased for nodes that are visited by a walk initiated from a first node and a second walk initiated from a second node, or scores may be decreased for nodes that are not visited by a first walk initiated from a first node and a second walk initiated from a second node. Content corresponding to nodes may be recommended based on the scores or visit counts.