Graph neural news recommendation
WebApr 14, 2024 · In this section, we first introduce our model framework and then discuss each module of KRec-C2 in detail. 3.1 Framework. The framework of our model is illustrated in Fig. 2, where we innovatively model context, category-level signals, and self-supervised features by three modules to improve the recommendation effect.KRec-C2 inputs … WebFeb 4, 2024 · This paper model the user-news interactions as a bipartite graph and proposes a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD, which can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation …
Graph neural news recommendation
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WebRecently, graph neural network (GNN) technology has been used more and more in recommender systems (Wu et al. 2024 ). The GNN-based recommendation model is … WebJul 18, 2024 · DAN: Deep Attention Neural Network for News Recommendation. The proposed DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention- based RNN for capturing richer hidden sequential features of user's clicks, and combines these features for new recommendation.
WebFeb 2, 2024 · Attention-Based Graph Neural Network for News Recommendation. In IJCNN. IEEE, 1–8. [11] Zhenyan Ji, Mengdan Wu, Hong Yang, and José Enrique Armendáriz Íñigo. 2024. Temporal sensitive heterogeneous graph neural network for news recommendation. Future Generation Computer Systems (2024). WebOct 30, 2024 · Graph Neural News Recommendation with Long-term and Short-term Interest Modeling. With the information explosion of news articles, personalized news …
WebDec 1, 2024 · This paper proposes a temporal sensitive heterogeneous graph neural network recommendation model, which considers the user’s historical click sequence … WebOct 30, 2024 · To address the above issues, in this paper, we propose a novel Graph Neural News Recommendation model (GNewsRec) with long-term and short-term user interest modeling.We first construct a heterogeneous user-news-topic graph as shown in Figure 2 to explicitly model the interactions among users, news and topics with complete …
WebJan 4, 2024 · Graph Neural Networks (GNN) have shown remarkable performance in different tasks. However, there are a few studies about GNN on recommender systems. GCN as a type of GNNs can extract high-quality embeddings for different entities in a graph.
WebApr 1, 2024 · In this paper, we develop a deep multi-graph neural network with attention fusion for recommender systems, termed MAF-GNN. Firstly, to obtain preferable latent representations for users and items, a dual-branch residual graph attention module is proposed to extract neighbor features from social relationships and knowledge graphs. electric charges and fields handwritten notesWebFeb 4, 2024 · This paper model the user-news interactions as a bipartite graph and proposes a novel Graph Neural News Recommendation model with Unsupervised … foods that are sweet and savoryWebNov 2, 2024 · Enhancement of the explainability by knowledge graph. As an external knowledge carrier with high readability, the knowledge graph brings a great opportunity to improve the explanation of the algorithm. The existing recommendation explanations are usually limited to one of three forms: item-mediated, user-mediated, or feature-mediated. electric charges and fields notes class 12WebIn this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. 5 Paper Code … foods that are three lettersWebChuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang: Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation. Findings of ACL 2024. Chuhan Wu, Fangzhao Wu, … electric charges comparisonWebApr 14, 2024 · By reformulating the social recommendation as a heterogeneous graph with social network and interest network as input, DiffNet++ advances DiffNet by injecting both the higher-order user latent ... foods that are the color orangeWebRecently, with the rise of graph convolution neural network, because graph neural network strong learning ability from non-Euclidean data and most of the data in real recommendation scenarios are non-Euclidean structure, graph convolutional neural network (GCN) model has also made considerable achievements in recommendation … foods that are unhealthy for dogs