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Graph-Based Recommendation
Recommendation system approach using graph structures to model complex relationships between users and items. This method captures multiple interactions and implicit connections to improve the relevance of recommendations.
Knowledge Graph
Data structure representing entities and their semantic relationships as nodes and edges. Used in recommendation systems to enrich item representations with contextual information.
Graph Neural Networks
Deep learning architecture specially designed to process graph-structured data. These networks perform convolution operations on nodes to capture relational dependencies.
Node Embedding
Dense vector representation of graph nodes in a low-dimensional space. These embeddings capture the structural and semantic properties of nodes to facilitate recommendation tasks.
Graph Convolutional Network
Type of neural network applying convolution operations directly on graph structures. Propagates information between neighboring nodes to learn hierarchical representations.
Heterogeneous Information Network
Graph containing multiple types of nodes and/or edges representing different entities and relationships. Models complex systems with multi-type interactions like recommendation platforms.
Matrix Factorization
Matrix decomposition technique used to discover latent features in recommendation data. Integrated in graph-based approaches to improve the representation of user-item interactions.
Bipartite Graph
Graph structure with two distinct sets of nodes where edges only connect nodes from different sets. Fundamental for modeling user-item relationships in recommendation systems.
Graph Embedding
Process of transforming the topological structure of a graph into continuous vector representations. Enables the application of traditional machine learning algorithms to graph data.
Graph Attention Network
GNN architecture incorporating attention mechanisms to differently weight contributions from neighboring nodes. Improves performance by focusing on the most relevant relationships.
Session-Based Recommendation
Recommendation approach using temporal interactions of a single user session. Modeled as a dynamic graph where nodes represent sequentially visited items.
Cold Start Problem
Major challenge in recommendation when lacking historical data for new users or items. Graph-based approaches mitigate this problem by exploiting structural relationships and metadata.
Knowledge Graph Embedding
Specific technique for vectorially representing entities and relationships of a knowledge graph. Preserves semantic information to enrich recommendation systems.
Graph Traversal
Algorithm exploring nodes and edges of a graph according to specific rules. Used in recommendation to discover relevant paths between users and items.
Node Classification
Task consisting of assigning labels to graph nodes based on their characteristics and relationships. Applied to categorize users or items in recommendation systems.
Link Prediction
Problem of predicting the future existence of links between pairs of nodes in a graph. Essential for anticipating user-item interactions and generating relevant recommendations.
Graph Sampling
Technique for selecting representative subgraphs to reduce computational complexity. Crucial for handling large-scale graphs in large-scale recommender systems.