Graph-Based Movie Recommendation System

Overview

The movie recommendation problem can be effectively addressed using graph-based algorithms, particularly Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). These approaches leverage the inherent relationships between users and movies to generate precise and personalized recommendations.

In this system, users and movies are represented as nodes in a bipartite graph, with edges signifying user interactions (e.g., clicks or ratings). By modeling these interactions, GCN and GAT algorithms capture complex user-movie relationships and make recommendations based on both direct and higher-order connections.

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Key Features

• Higher-Order Relationships
  GCNs and GATs analyze not just direct user-movie interactions but also their broader network of relationships, uncovering intricate patterns.

• Contextual Variability
  The models adapt to users' diverse interests and behaviors by learning from their network connections and interactions.

• Class Imbalance Management
  Graph-based algorithms handle the imbalance caused by the sparse reviews in movie datasets, ensuring robust recommendations.

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Methodology

To enhance recommendation accuracy, the system employs two prediction methods:

1. Click Prediction using LightGCN

• LightGCN is a lightweight Graph Convolutional Network designed to model user-movie interactions efficiently.
• It captures latent patterns in the data, enabling the system to predict which movies users are likely to click on.

2. Rating Prediction using IGMC and GraphRec

• Inductive Graph-Based Matrix Completion (IGMC) and GraphRec models are built on Graph Attention Networks (GATs).
• These models incorporate explicit user-movie interaction data to provide personalized rating predictions.

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Advantages

• Models higher-order neighborhoods for better pattern recognition.
• Captures the variability in user preferences.
• Effectively manages sparse datasets and class imbalance issues.

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Conclusion

By integrating cutting-edge graph-based algorithms like GCNs, GATs, and LightGCN, this system delivers accurate and high-quality movie recommendations. It considers not only user preferences but also the relational and contextual nuances in the data.

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References

1. GCNs and GATs for graph-based recommendation systems.
2. LightGCN Paper
3. IGMC and GraphRec for rating prediction models.