Unequal distribution of healthcare resources poses a significant challenge in numerous regions and countries worldwide. Online Health Communities (OHCs) serve as pivotal platforms for patients to share treatment experiences and seek emotional ...

The recommender system recommends items to the users based on their preferences of implicit feedback. However, implicit feedback often contains noise that deviates from the user’s true preferences, thereby influencing the accuracy of the ...

• A recommender system that adapts to noise in implicit feedback.
• Balancing accuracy and diversity through data collaborative training.
• Experiments on three datasets verify the effectiveness of the model.

Traditional collaborative filtering recommendation algorithms suffer from low recommendation efficiency and poor accuracy when calculating similarities between users or items. To address this issue and improve the efficiency of recommendation ...

With the exponential growth of video and other content across various domains including entertainment, e-commerce, education and social media, there is a growing need for personalized content recommendations that are relevant to users’ interests. ...

COVID-19 has significantly impacted individuals, communities, and countries worldwide. These effects include health impacts, economics impacts, social impacts, educational, political and environmental impacts. The COVID-19 vaccine development was ...

The mainstream approach of GNN-based recommendation aggregates high-order ID information associated with the node in the user-item graph. The aggregation pattern using ID as signal has two disadvantages: lack of textual semantics and the impact of ...

Recommender systems enhanced by a knowledge graph (KG) have attained widespread popularity and attention in recent years. However, traditional KG-based recommender systems encounter the challenge of gradient explosion as the network depth ...

The knowledge graph convolution network (KGCN) is a recommendation model that provides a set of top recommendations based on knowledge graph developed between users, items, and their attributes. In this study, we integrate the KGCN model with ...

• The KGCN is a recommendation model that provides a set of top recommendations.
• Our framework (KGCN-DAE) integrates the KGCN model with denoising autoencoder.
• The KGCN-DAE improves the performance and efficiency of the KGCN model.

Neural network session-based recommendation (SBR) models are challenged to capture transformation relationships in a chain of anonymous user activities (i.e., interaction) to predict the next interact item in the session. However, under the ...

Most of the existing recommendation methods are based on shallow models that do not consider rating interaction information noise. To solve this problem, this paper proposes a recommendation model based on deep nonnegative matrix factorization (...${\mathcal{}}_{\mathrm{}\mathrm{}}$

Recently, it has become common knowledge that using reinforcement learning for a sequential recommendation, which predicts a user’s next action, can improve recommendation performance. This is because reinforcement learning can be used to ...

In light of the remarkable capacity of graph convolutional network (GCN) in representation learning, researchers have incorporated it into collaborative filtering recommendation systems to capture high-order collaborative signals. However, ...

A primary strategy for constructing click-through rate models based on deep learning involves combining a multi-layer perceptron (MLP) with custom networks that can effectively capture the interactions between different features. This is due to ...

Providing system-generated explanations for recommendations represents an important step toward transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the past two ...

The variety of enormous information on the web encourages the field of recommender systems (RS) to flourish. In recent times, deep learning techniques have significantly impacted information retrieval tasks, including RS. The probabilistic and ...

As a critical task for large-scale commercial recommender systems, reranking rearranges items in the initial ranking lists from the previous ranking stage to better meet users’ demands. Foundational work in reranking has shown the potential of improving ...

Extending from sequential recommendation models, in this article, we present a novel framework named Search-based Time-Aware Recommendation (STARec), which first retrieves the historical behaviors of the given user through a search-based retriever and ...

Recommender system helps address information overload problem and satisfy consumers’ personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy ...

Collaborative filtering (CF) is a typical and widely used recommendation method. The main idea is to recommend to the target user items that users similar to him/her like. CF has two main challenges. One is that users’ ratings of items are sparse and ...

Popularity bias is commonly observed in recommendation results. Directly fitting biased data can significantly affect the quality of recommendations for long-tail items. To eliminate popularity bias, we propose a novel recommendation method ...