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Improving Sequential Recommender Systems with AutoMLP: A Solution to Long/Short-Term Interest Identification

In recent years, sequential recommender systems have emerged as a popular approach for providing personalized recommendations to users. These systems take into account the order in which items are consumed by users, enabling them to identify long-term and short-term interests of users and predict their future preferences accurately. However, while sequential recommender systems have proven …

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Transform Your Recommender System with Temporal Graph Neural Networks

Introduction Recommender systems have revolutionized the way we discover new products, music, movies, and even potential romantic partners. They analyze users’ past interactions with items and provide personalized recommendations based on their preferences. However, traditional recommender systems face several challenges, such as sparsity, cold-start, and scalability. To overcome these limitations, researchers have been exploring the …

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Building Recommender Systems with Graph Generative Models

Introduction The explosion of data in recent years has led to an ever-increasing need for effective recommender systems. These systems are designed to analyze user behavior and preferences and provide recommendations for products, services, or content that are likely to be of interest to them. One of the most promising approaches to building these systems …

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