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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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Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System

In today’s world, e-commerce has become an integral part of our lives, and the success of these platforms relies on providing personalized recommendations to users. Recommender systems, therefore, play a crucial role in providing these personalized recommendations, and are used extensively in various industries, including music, movies, and online shopping. Recommender systems utilize users’ past …

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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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