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A Graph Neural Network Model for Diverse E-Commerce Recommendations
Using Timestamp-Weighted Edges
Conference Paper · December 2024
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A Graph Neural Network Model for Diverse
E-Commerce Recommendations Using
Timestamp-Weighted Edges
1st Abderaouf Bahi
Computer Science and Applied
Mathematics Laboratory
Chadli Bendjedid El Tarf University
El Tarf 36000, Algeria
4st Ramzi Khantouchi
Computer Science and Applied
Mathematics Laboratory
Chadli Bendjedid El Tarf University
El Tarf 36000, Algeria
2st Ibtissem Gasmi
Computer Science and Applied
Mathematics Laboratory
Chadli Bendjedid El Tarf University
El Tarf 36000, Algeria
3st Sassi Bentrad
LISCO Laboratory
National Higher School
of Cyber Security (NSCS), Algeria
Algiers, Algeria
Abstract— In e-commerce, personalized recommendation
systems are essential for guiding users through extensive
product catalogs. However, traditional recommendation
models often fail to offer diverse suggestions, leading to user
dissatisfaction and limited product exploration. This work
proposes a Graph Neural Network (GNN) model using
timestamp-weighted edges to improve recommendation
diversity. Timestamp weighting assigns greater importance to
recent interactions, capturing evolving user preferences while
maintaining high relevance. We evaluate the model on the
Amazon dataset, comparing accuracy and intralist diversity
metrics. Results show that timestamp-weighted edges boost
diversity, enabling a richer user experience and effective
exploration within e-commerce platforms.
Keywords—E-commerce recommendation, Graph Neural
Network, timestamp-weighted edges, recommendation diversity.
I. INTRODUCTION
In today’s rapidly evolving digital economy, e-commerce
platforms have become indispensable in connecting
consumers to an immense range of products, from everyday
essentials to niche specialty items. These platforms host vast
product catalogs and attract diverse user bases, each with
unique needs and preferences. As users engage with
e-commerce platforms, recommendation systems [1] play a
critical role in helping them navigate these extensive
catalogs by providing tailored suggestions based on their
past behaviors [2], preferences [3], and interactions [4]. By
leveraging large amounts of data, recommendation systems
have transformed the online shopping experience, enabling
personalized shopping journeys that can increase user
satisfaction, foster brand loyalty, and drive sales.
However, despite their widespread success, many traditional
recommendation systems [5] exhibit limitations, particularly
in their ability to provide diverse suggestions. This lack of
diversity arises from the tendency of recommendation
algorithms to prioritize popular or frequently interacted-with
items, often resulting in homogenous recommendations [6]
that fail to represent the full spectrum of the platform’s
offerings. Consequently, users may encounter a narrow set
of items that restricts their exploration and fails to meet their
broader interests. By focusing primarily on relevance, these
systems risk overfitting to immediate user preferences [7],
thereby limiting exposure to novel or lesser-known
products. This can lead to user dissatisfaction, increased
churn rates, and missed opportunities for the platform to
showcase its comprehensive product range.
A diverse recommendation system [8], on the other hand,
can significantly enhance the user experience by introducing
a broader selection of products [9] that cater to various
facets of a user’s interests. Diversity in recommendations is
beneficial not only for individual users but also for platform
sustainability, as it encourages product discovery, reduces
redundancy, and keeps users engaged. Achieving this
diversity, however, presents a unique challenge: how to
balance relevance with variety in a way that respects user
preferences without overemphasizing any single product or
category.
Graph Neural Networks (GNNs) have recently emerged as a
powerful solution in the recommendation domain [10] due
to their ability to capture complex relationships between
users, items, and various forms of interactions. GNNs model
these interactions as nodes and edges in a graph, enabling
the recommendation system to leverage both direct and
indirect relationships within the data. This graph-based
approach allows for a more nuanced representation of user
preferences and item features, as GNNs can aggregate
information from multiple nodes, detect patterns across
different paths, and understand the underlying structure of a
network. GNN-based models excel in scenarios where data
relationships are complex, as in e-commerce, where user
preferences may be influenced by both immediate and
historical interactions across multiple product types.
Traditional GNN-based recommendation systems [11],
however, often prioritize accuracy and relevance, focusing
on optimizing for metrics like click-through rates and user
satisfaction scores. While effective for generating relevant
recommendations, this approach can inadvertently introduce
biases that favor popular items or reinforce established
preferences. As a result, these models may lack the
flexibility to offer diverse suggestions, ultimately limiting
the variety of products shown to users. This homogeneity
restricts user discovery and can result in user
disengagement, as the recommendations become predictable
and repetitive [12].
To address these limitations, we propose a novel GNN
model that incorporates timestamp-weighted edges to
enhance the diversity of recommendations.
XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE
Timestamp-weighted edges allow the model to assign
varying levels of importance to user-item interactions based
on their recency, creating a dynamic view of user
preferences that adapts over time. By weighting more recent
interactions more heavily, this approach ensures that
recommendations are relevant to current interests, while still
providing diversity by considering a range of products
interacted with over time. This method strikes a balance
between exploring new products and exploiting known
preferences, encouraging users to engage with a wider array
of offerings.
In this study, we explore the impact of timestamp-weighted
edges in a GNN model to improve recommendation
diversity without compromising relevance. We hypothesize
that this approach will lead to a more balanced and varied
recommendation list, offering users new and interesting
products that align with their evolving preferences. Through
extensive experimentation, we examine how
timestamp-weighted edges affect diversity metrics, such as
novelty and intralist similarity, alongside traditional
relevance measures. Our findings aim to provide insights
into the advantages of incorporating temporal dynamics into
graph-based recommendation systems, underscoring the
potential of timestamp-weighted GNNs to drive both
diversity and user satisfaction in e-commerce applications.
The remainder of this paper is structured as follows. Section
2 provides an overview of related work in the field of
recommendation systems, particularly focusing on
reinforcement learning-based approaches. In Section 3, we
describe the methodology used in this study, detailing the
dataset, preprocessing steps, reinforcement learning
algorithm, and evaluation metrics. Section 4 presents the
experimental setup, including the design of the RL agent
and the training process, followed by an analysis of the
results. The paper concludes with a discussion of the
findings and potential directions for future research in
Section 5.
II. RELATED WORK
Graph Neural Networks (GNNs) are redefining the
landscape of e-commerce recommender systems by offering
a sophisticated framework for understanding and leveraging
complex interactions among users, products, and
transactions in large-scale networks. By capturing
high-dimensional and interrelated information, GNNs allow
for enhanced personalization and recommendation diversity
[13], meeting the evolving needs of digital commerce.
One notable work in this space is by Xia et al. [14], who
investigated the role of dynamic user-item interaction
learning in scenarios where multiple types of interactions
(e.g., views, purchases, and clicks) occur. They introduced
the Temporal Graph Transformer (TGT), a model
specifically designed to capture both short-term and
long-term user behavior trends by analyzing the changing
connections between users and items across various
interaction types. This model enables recommender systems
to extract not only type-specific contextual information but
also dependencies across different types of behaviors,
providing a more nuanced understanding of user
preferences. Tested on real-world datasets, TGT
demonstrated that incorporating dynamic, multi-behavior
patterns into recommendations can enhance accuracy and
personalization.
Weiwen et al. [15] extended the GNN approach by
emphasizing the importance of understanding product
relationships to improve both the accuracy and
interpretability of e-commerce recommendations. Their
work introduced the Item Relationship Graph Neural
Network (IRGNN), which captures multi-faceted
relationships across a product network. Unlike models that
focus solely on direct product-to-product links, IRGNN
explores the deeper topological structure, uncovering latent
connections and multi-hop paths within a product graph.
This method involves recursive node embedding updates
and an edge relational network to track relational data
between products, even in sparse datasets. Their
experiments, conducted on extensive product datasets, show
that IRGNN efficiently improves recommendations on
large, sparse networks by capturing complex relationships
that go beyond simple co-purchase patterns.
In a complementary direction, Zhao et al. [16] addressed the
shared challenges in click-through rate (CTR) prediction for
both search and recommendation tasks within e-commerce
platforms. They developed the Search and Recommendation
Joint Graph (SRJGraph), a model that integrates the search
and recommendation environments into a single, cohesive
graph. SRJGraph represents search queries, items, and users
as heterogeneous nodes, facilitating knowledge sharing
across both contexts. The model incorporates an intention-
and upstream-aware aggregator to explore higher-order
connections, particularly beneficial for sparse user
interaction scenarios. Extensive testing on Taobao’s
large-scale e-commerce dataset confirmed SRJGraph’s
effectiveness, outperforming state-of-the-art models in CTR
prediction across search and recommendation tasks, and
showcasing its potential to improve multi-scenario
e-commerce applications.
Together, these studies underscore the versatility and
potential of GNNs in e-commerce, demonstrating that by
modeling complex relationships and dynamic interactions,
GNN-based recommender systems can achieve higher
accuracy, better personalization, and greater interpretability.
III. METHODOLOGY
In this section, we outline the methodological approach
employed to develop a Graph Neural Network (GNN)
model that leverages timestamp-weighted edges to enhance
the diversity of recommendations in an e-commerce context.
Our methodology consists of several key steps: data
processing, graph construction, and GNN training. Each
step is designed to handle unique aspects of the data and
optimize the model’s ability to capture both relevant and
diverse recommendations. We focus on transforming raw
e-commerce data into a structured, graph-based format that
allows the GNN to capture temporal dynamics and complex
user-product interactions, ultimately improving the system's
capacity for varied recommendations.
A. Data processing
In this phase, we prepare the raw interaction data for
graph-based learning. The data processing steps include
filtering, transformation, and feature engineering
represented in Algorithm 1.
Algorithm 1: Data processing
# Step 1: Data Filtering
FOR each interaction IN dataset:
IF interaction is too old OR user/item is inactive:
REMOVE interaction
# Step 2: Data Transformation
user_item_matrix = Initialize empty matrix
FOR each interaction IN filtered_dataset:
Add interaction to user_item_matrix
Assign timestamp as an attribute to the interaction
# Step 3: Feature Engineering
FOR each user IN dataset:
Calculate interaction frequency
Calculate demographic data (age, location, etc.)
Assign as user node attributes
FOR each item IN dataset:
Extract item category, brand, average rating
Assign as item node attributes
B. Graph Construction
With the processed data, we create a graph where users and
items are nodes and interactions form timestamp-weighted
edges. This graph structure allows the GNN to model the
temporal dynamics and complex relationships between users
and items. represented in Algorithm 2.
Algorithm 2: Graph Construction
# Step 1: Node Representation
graph = Initialize empty graph
FOR each user IN user_item_matrix:
Create a node for user with user-specific attributes
FOR each item IN user_item_matrix:
Create a node for item with item-specific attributes
# Step 2: Edge Creation and Weighting
FOR each interaction (user, item, timestamp) IN user_item_matrix:
edge_weight = CalculateTimestampWeight(timestamp)
Create edge between user node and item node with edge_weight
# Step 3: Temporal Decay Function
Define TemporalDecayFunction(timestamp, current_time):
# Exponential decay based on time difference
time_difference = current_time - timestamp
decay_rate = Predefined decay parameter
RETURN exp(-decay_rate * time_difference)
CalculateTimestampWeight(timestamp):
RETURN TemporalDecayFunction(timestamp, current_time)
C. GNN Training
We train the GNN on the graph, with timestamp-weighted
edges guiding the propagation and aggregation of
information. The model’s architecture captures complex
relationships, allowing for diverse and relevant
recommendations. represented in Algorithm 3.
Algorithm 3: GNN Training
# Step 1: Model Architecture Initialization
Initialize GNN with multiple graph convolutional layers
Define attention mechanism within each layer
# Step 2: Propagation and Aggregation
FOR each layer IN GNN:
FOR each node IN graph:
neighbor_nodes = GetNeighbors(node)
edge_weights = GetEdgeWeights(neighbor_nodes, node)
# Apply attention weighting based on edge weights
attention_weights = CalculateAttention(edge_weights)
# Aggregate information from neighbors
aggregated_info = Aggregate(attention_weights, neighbor_nodes)
# Update node embeddings with aggregated information
node_embedding[node] = UpdateEmbedding(node, aggregated_info)
# Step 3: Loss Function and Diversity Metrics
Define LossFunction(predictions, targets):
relevance_loss = CalculateRelevanceLoss(predictions, targets)
diversity_loss = CalculateDiversityLoss(predictions)
RETURN relevance_loss + diversity_loss
Define DiversityMetrics(predicted_recommendations):
novelty = CalculateNovelty(predicted_recommendations)
coverage = CalculateCoverage(predicted_recommendations)
intralist_diversity=CalculateIntraListDiversity(predicted_recommendat
ions)
RETURN novelty, coverage, intralist_diversity
# Step 4: Optimization and Tuning
WHILE model not converged:
predictions = GNN.forward_pass(graph)
loss = LossFunction(predictions, targets)
# Backpropagation and optimization step
UpdateModelParameters(loss)
# Tune hyperparameters such as learning rate, number of layers
OptimizeHyperparameters()
IV. EXPERIMENTAL EVALUATION
In this section, we describe the experimental setup used to
evaluate the proposed GNN model, with a focus on
assessing the diversity and relevance of recommendations.
A. Data description
The Amazon Product Dataset contains millions of user
interactions with products across multiple categories,
making it an ideal dataset for evaluating recommendation
diversity and relevance. Key characteristics of the dataset
are as follows:
●User-Item Interactions: Contains user reviews, ratings,
and timestamps of interactions with products, allowing
us to model user behavior over time.
●Product Attributes: Includes detailed product
information, such as categories, brands, and prices,
which are used as item features in the GNN model.
●Temporal Data: The dataset includes timestamps of
interactions, which are essential for applying the
timestamp-weighted edge approach to model recent
user preferences effectively.
B. Baseline models
To assess the effectiveness of our proposed model, we
compared its performance with several baseline
recommendation models:
●Matrix Factorization (MF): A classical collaborative
filtering method widely used in recommender systems.
While it performs well in accuracy, it typically lacks in
diversity.
●Content-Based Filtering (CBF): This model leverages
product attributes to generate recommendations. While
it improves diversity by considering item features, it
may not fully capture user preferences.
●Graph Convolutional Network (GCN): A GNN model
without temporal weighting, focusing on capturing
user-item interactions based on graph structure. This
model provides strong accuracy but may overlook the
temporal relevance of interactions.
C. Evaluation metrics
We chose to evaluate our model, two important and
well-known evaluation metrics in the field of machine
learning and recommender systems, allowing for
comparison with other cutting-edge approaches.
Accuracy: We employ accuracy as a general measure of
performance. It is calculated by determining the proportion
of correct predictions (both true positives and true
negatives) relative to the total number of predictions made.
Formula (1) illustrates the mathematical expression for this
metric.
(1)
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 + 𝑇𝑁
𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
Intra-list diversity: It measures the diversity within the set of
recommended items. It evaluates the difference between
items, ensuring that recommendations are not too similar
and providing a wide range of options to the user. This
measure is represented by Formula (2).
(2)
𝐼𝐿𝐷= 1− 1
𝑛×(𝑛−1)𝑖=1
𝑛
∑/𝑗=1,𝑗≠𝑖
𝑛
∑ 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑖𝑡𝑒𝑚𝑖,𝑖𝑡𝑒𝑚𝑗)
D. Results
Our experiments were conducted by training each model on
the Amazon Product Dataset and evaluating performance on
a held-out test set. Results were obtained as represented in
Figure 1.
Figure 1. Models performance comparison
The Proposed GNN model with timestamp-weighted edges
shows improved accuracy over most baseline models, along
with the highest diversity score, indicating its effectiveness
in delivering diverse yet relevant recommendations. This
balanced performance highlights the model's ability to adapt
to users’ recent interests while ensuring varied product
exposure.
V. Conclusion
This study demonstrates the potential of incorporating
timestamp-weighted edges within a Graph Neural Network
model to enhance the diversity of recommendations in
e-commerce platforms. By prioritizing recent interactions,
the proposed model effectively balances relevance with
diversity, encouraging users to explore a wider array of
products. Experimental results indicate that timestamp
weighting significantly improves intralist diversity without
sacrificing recommendation accuracy, offering a promising
approach for e-commerce platforms aiming to enhance user
satisfaction and discovery.
ACKNOWLEDGMENT
The authors express their gratitude to the Algerian
Ministry of Higher Education and Scientific Research
(MESRS) for financially supporting the PRFU Research
Project (PhD Thesis), coded: C00L07UN360120230002,
conducted at Chadli Bendjedid El Tarf University, Algeria.
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