Overview of GNN-based recommendation techniques and related algorithms and implementation examples.

Overview of GNN-based recommendation technology

Graphs are expressive and powerful data structures that are widely applicable due to their flexibility and effectiveness in modelling and representing graph-structured data, and are becoming increasingly popular in fields as diverse as biology, finance, transport and social networks.

Recommender systems are one of the most successful commercial applications of artificial intelligence, where user-item interactions can be naturally adapted to graph-structured data, and have attracted significant attention in the application of graph neural networks (GNNs).

This section describes a recommender system using GNNs.

First, the basic capability required for recommender systems is to predict the likelihood that a user will take action on a particular display item, and items in a recommender system can include news recommendations, e-commerce recommendations, video recommendations, etc., and for these The recommender system is ultimately optimised for a complex ecosystem of multifaceted participants – users, platforms and content providers – to maximise utility on the part of the user.

Graph neural network (GNN)-based recommendation technology models the relationship between users and items in a graph structure and improves recommendation accuracy through information propagation on top of it.

This section first describes the basic concept of GNNs.

A GNN is a neural network that handles graph data and processes graphs consisting of nodes (points) and edges (lines), where nodes represent users and items and edges represent relationships between them (e.g. a user has purchased or rated an item).

The GNN works in the following steps.

1. initialisation: assign an initial feature vector to the node. User and item feature and historical data are included here.
2. information propagation (message passing): each node receives information from neighbouring nodes and updates its own feature vector. This process is repeated several times so that a node’s feature vector reflects its neighbourhood.
3. aggregation: the feature vectors of the final nodes are used to perform a prediction task (e.g. predicting which items to recommend next).

Typical approaches for applying this GNN to recommendation systems include.

(1) Node embedding: user and item nodes are converted into embedding vectors and recommendations are made based on these. For example, the inner product of the embedding vectors of the user and item is calculated and the item with the higher value is recommended.

(2) Construction of a connected graph: users and items are connected to a single graph and GNN is applied to learn the relationship between users and items simultaneously. This method can capture the interaction between users and items more precisely.

(3) Layered approach: learning deeper relationships by layering the graphs. For example, not only the direct relationship between user and item, but also indirect relationships through common items are taken into account.

GNN-based recommendation techniques have the advantage that GNNs can naturally model complex relationships between users and items, can efficiently process large data sets, have good generalisation capabilities for unknown users and items, and are expected to improve recommendation accuracy compared to traditional methods The approach is expected to improve recommendation accuracy compared to traditional methods.

Algorithms related to GNN-based recommendation techniques

The main algorithms associated with GNN-based recommendation techniques include the following. These algorithms utilise the properties of GNNs to effectively learn the relationship between users and items.

1. Graph Convolutional Networks (GCNs): GCNs update the feature vector of each node by performing convolutional operations on the graph of users and items, which generates embeddings that reflect the relationship between users and items, making them effective in recommendation systems. It is effective in recommendation systems. For more information on GCNs, see “Overview, Algorithm and Implementation of Graph Convolutional Neural Networks (GCNs)“.

2. GraphSAGE (Graph Sample and Aggregation): GraphSAGE involves sampling the neighbour nodes of each node, aggregating the features and updating the node representation. This method is suitable for processing large graphs. For more information on GraphSAGE, see GraphSAGE Overview, Algorithm and Implementation Examples.

3. PinSAGE: PinSAGE is an algorithm developed at Pinterest, based on GraphSAGE and dedicated to recommending items on large graph datasets. This is a particularly useful approach for image and content recommendations. Features include a combination of content-based filtering and collaborative filtering, and the ability to scale to large datasets.

4. Graph Attention Networks (GATs): GATs learn the importance of neighbouring nodes using an attention mechanism (attention mechanism), which updates node features and places more emphasis on important node information. For more information on GATs, see “Overview of GATs (Graph Attention Networks), algorithms and implementation examples“.

5. Neural Graph Collaborative Filtering (NGCF): NGCF applies the concept of collaborative filtering to graph structures and learns the interaction between users and items at a deep level. It effectively models the relationship between users and items. Features include learning the bi-directional relationship between user and item, and propagating information in layers to capture the deep relationship.

6. LightGCN: LightGCN is a lightweight version of NGCF that balances computational efficiency and accuracy and provides high performance while saving computational resources, especially in recommender systems. Features include improved computational efficiency and high accuracy despite a simple model structure.

7.GraphRec: GraphRec is a graph-based recommendation algorithm for learning direct and indirect relationships between users and items, effectively learning features of both users and items. Features include integration of both user and item graph information and high expressiveness and flexibility.

8. Collaborative Filtering Neural Network (CFNet): CFNet is an algorithm that combines traditional collaborative filtering techniques with a neural network, which learns to embed users and items. This enables highly accurate prediction of user preferences. Features include enhanced effects of collaborative filtering and learning of higher-order relationships using neural networks.

These algorithms utilise the characteristics of GNNs to effectively learn the relationship between users and items, enabling highly accurate recommendations.

Applications of GNN-based recommendation technology

Recommender technology has been applied in a variety of fields. Specific applications are described below.

1. e-commerce:

Case study: Amazon
Approach: Amazon models the relationship between users and items as a graph based on the user’s purchase and browsing history, and recommends products that are likely to be purchased next using GNN.
Effectiveness: deep understanding of users’ purchasing patterns enables personalised and highly accurate product recommendations.

2. social media:

Case study: Facebook
Approach: Facebook is a model of user friendships, ‘Likes’, comments and other interactions as a graph. and comments are modelled as graphs, and friend and content recommendations are made using GNNs.
Effectiveness: personalised content based on users’ interests increases engagement.

3. video streaming:

Case study: Netflix
Approach: Netflix models the relationship between users and videos as a graph based on users’ viewing history and rating data, and recommends videos they are likely to watch next using GNN.
Effectiveness: Netflix recommends films and TV programmes that match viewers’ preferences with a high degree of accuracy, improving the viewing experience.

4. music streaming:

Case study: Spotify
Approach: Spotify models the relationship between users and songs as a graph based on the user’s playback history and playlist information, and recommends songs and artists that the user is likely to listen to next using GNN.
Effectiveness: personalised recommendations based on users’ music preferences, increasing user engagement.

5. online advertising:

Case study: Alibaba
Approach: Alibaba models the relationship between users and ads as a graph based on users’ click and purchase histories, and uses GNNs to recommend ads that are highly relevant to users.
Effectiveness: the company is improving ad click-through rates and maximising ad revenues.

6. job recommendation:

Case study: LinkedIn
Approach: LinkedIn models the relationship between users and jobs as a graph based on users’ profile information, browsing history and application history, and recommends suitable jobs to users using GNNs.
Effectiveness: the system provides appropriate job information to job seekers and improves the accuracy of job matching.

7. game recommendation:

Case study: Steam
Approach: Steam models the relationship between users and games as a graph based on users’ gameplay history and rating data, and recommends games that users are likely to play next using GNNs.
Effectiveness: recommendations of games based on gamers’ interests are made with high accuracy, increasing engagement on the platform.

8. education sector:

Case study: Coursera
Approach: Coursera models the relationship between users and courses as a graph based on users’ course history and evaluation data, and uses GNN to recommend courses that they are likely to take next.
Efficacy: the system recommends courses based on learners’ interests and skill levels, improving the learning experience.

By applying GNNs to these applications, it is possible to utilise the powerful relationship modelling capabilities of GNNs to provide personalised recommendations to users and improve engagement.

Examples of GNN-based implementation of recommendation techniques

The implementation of GNN-based recommendation techniques is usually done using Python libraries. A basic implementation example is given below. In this example, PyTorch and PyTorch Geometric are used to build a user and item recommendation system.

Installing the required libraries: first, install the required libraries.

pip install torch
pip install torch-geometric

Data preparation: here, simple user/item relationships are prepared as graphical data.

import torch
from torch_geometric.data import Data

# Define the relationship between users and items as an edge list.
edge_index = torch.tensor([
    [0, 0, 1, 1, 2, 2, 3, 3],
    [0, 1, 0, 2, 1, 3, 2, 3]
], dtype=torch.long)

# Node features (here simply a one-dimensional vector)
x = torch.tensor([
    [1], [2], [3], [4]
], dtype=torch.float)

# Creation of graphical data
data = Data(x=x, edge_index=edge_index)

Defining the model: defining the model using a Graph Convolutional Network (GCN).

import torch.nn.functional as F
from torch_geometric.nn import GCNConv

class GCN(torch.nn.Module):
    def __init__(self):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(1, 16)  # Input dimension 1, output dimension 16.
        self.conv2 = GCNConv(16, 16)  # 16 input dimensions, 16 output dimensions.

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = self.conv2(x, edge_index)
        return x

model = GCN()

Training the model: the next step is to train the model. Here, the MSE is used as a simple loss function.

import torch.optim as optim

# Definition of loss functions and optimisation methods.
criterion = torch.nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

# Preparation of training data (identical data used here for simplicity)
target = torch.tensor([
    [1], [2], [3], [4]
], dtype=torch.float)

# training loop
for epoch in range(100):
    model.train()
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

    if epoch % 10 == 0:
        print(f'Epoch {epoch}, Loss: {loss.item()}')

Performing recommendations: once the training is complete, the model is used to make recommendations. Here, the user’s feature vector is obtained as output and the item is recommended.

# Obtaining nomination results
model.eval()
with torch.no_grad():
    embeddings = model(data)

# Displays recommendation results for user 0 as an example
user_embedding = embeddings[0]
similarities = torch.mm(user_embedding.view(1, -1), embeddings.t())
recommended_item = torch.argmax(similarities).item()
print(f'Recommended item for user 0: {recommended_item}')

DESCRIPTION: This example uses a graph where users and items are nodes and their relationships are represented as edges, and GCN is used to learn the features of the nodes and finally recommend items to the user.

• Data preparation: the relationship between users and items is defined as a graph.
• Model definition: two-layer neural network using GCN.
• Training: simple training loop using MSE.
• Recommendation: recommendation of the most similar items using the learnt user embedding vectors.

In real applications, more complex data and models are used to train the model, e.g. including user and item features, historical data and even metadata. A variety of evaluation metrics are also used, including click and purchase rates and other metrics relevant to real businesses.

Challenges and remedies for GNN-based recommendation technology

Although GNN-based recommendation techniques are very powerful, several challenges exist. The main challenges and their respective countermeasures are described below.

1. high computational cost:

Challenge: GNNs require complex computations and are computationally expensive when processing large graph data.

Solution:
Use of sampling techniques: some techniques, such as GraphSAGE, reduce computational costs by sampling the neighbourhood of nodes.
Efficient model design: adopt approaches such as LightGCN that reduce the number of model parameters to increase computational efficiency.
Distributed processing: handling large data efficiently by distributing the data across multiple computation nodes.

2. data sparsity:

Challenge: in recommendation systems, it is common for user and item relationship data to be sparse. This leads to reduced recommendation accuracy, especially for new users and new items.

Solution:
Use of embedding techniques: use node embedding techniques to convert sparse data into dense vectors, effectively representing user and item characteristics.
Use of metadata: complement sparsity by adding user and item attribute information (e.g. age, gender, category) to the model.

3. dynamic changes in data:

Challenge: user preferences and item popularity change over time. GNNs with static graphs can have difficulty capturing these dynamic changes.

Solution:
Use dynamic GNNs: Dynamic GNNs (e.g. Temporal Graph Networks) can handle graph data that changes over time.
Online learning: employ online learning techniques that update the model each time new data arrives.

4. lack of interpretability:

Challenge: GNNs are black box models, making it difficult to understand why a particular item was recommended.

Solution:
Adopt explainable AI techniques: use techniques (e.g. Grad-CAM and SHAP) to identify key features and nodes that contribute to GNN predictions and explain the model’s predictions.
Using heuristic approaches together: complementing model predictions with intuitively understandable algorithms, such as collaborative filtering.

5. data privacy and security:

Challenge: privacy and security issues are important because the recommendation system deals with users’ personal data and behavioural history.

Solution:
Differential privacy: implement differential privacy technologies to protect users’ personal data.
Secure data management: strengthen data encryption and access control to prevent unauthorised access and information leakage.

6. model over-learning:

Challenge: complex GNN models over-learn on training data and have poor generalisation capabilities for new data.

Solution:
Regularisation methods: use dropout and L2 regularisation to prevent over-learning.
Cross-validation: cross-validate to assess model performance and check for signs of over-learning.

Reference Information and Reference Books

For more information on graph data, see “Graph Data Processing Algorithms and Applications to Machine Learning/Artificial Intelligence Tasks. Also see “Knowledge Information Processing Techniques” for details specific to knowledge graphs. For more information on deep learning in general, see “About Deep Learning.

Reference book is

“Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch“

“Graph Neural Networks: Foundations, Frontiers, and Applications“等がある。

“Introduction to Graph Neural Networks“

“Graph Neural Networks in Action“

Basics: recommender systems
1. 「Recommender Systems: The Textbook」
– Author: Charu C. Aggarwal
– An introduction to the basic concepts of recommender systems, covering collaborative filtering, content-based recommendation, etc. Useful as a foundation before applying GNNs.

2. 「Deep Learning for Recommender Systems」
– Authors: Alexandros Karatzoglou, Balázs Hidasi
– Focuses on recommendation techniques using deep learning, providing a foundation for deep learning as a prerequisite knowledge for GNNs.

GNN Fundamentals.
3. 「Graph Neural Networks: Foundations, Frontiers, and Applications」
– Author(s): Lingfei Wu, Peng Cui, Jian Pei
– This comprehensive book covers the basic concepts and applications of GNNs. It is useful as background knowledge when applied to recommendation systems. 4.

4. 「Deep Learning on Graphs」
– Author(s): Yao Ma, Jiliang Tang
– An introduction to deep learning on graph data. It is particularly suitable for learning key models of GNNs, such as Graph Convolutional Networks (GCNs).

Integrating Recommender Systems and GNNs.
5. 「Graph Neural Networks in Recommender Systems」
– Authors: Yuan Zhang, Miao Zhang
– Details how GNNs can be applied to recommender systems. It deals with an approach where user-item relationships are represented as graphs and learnt by GNNs.

6. 「Graph Neural Networks for Recommender Systems」 (collected papers format).
– A collection of papers on state-of-the-art techniques for GNNs and recommender systems, useful for understanding recent research trends.

For practical use.
7. 「Hands-On Graph Neural Networks with Python」
– Author(s): Vishal Pratap Singh, Gaurav Aggarwal
– Extensive examples of GNN implementations using Python. Can be used as concrete implementation examples when trying to apply to recommendation systems.

8. 「Graph Machine Learning: Learn about the latest advancements in Graph data to build robust machine learning algorithms」
– Author(s): Jie Tang, Meng Jiang
– Covers the theory and practice of graph machine learning in general, including GNNs. Some of the applications in recommendation systems are also explained.

Paper resource.
– “PinSage: A Graph Convolutional Network for Web-Scale Recommender Systems”
– Paper presenting a practical application of GNNs, with examples used in Pinterest.

– “Graph Neural Networks for Social Recommendation”
– A study of recommendation techniques using social networks between users.