Graph neural news recommendation
WebDec 26, 2024 · A curated list of graph reinforcement learning papers. GNN Papers Enhance GNN by RL 2024 2024 2024 2024 Enhance RL by GNN 2024 2024 TODO 2024 TODO Non-GNN Papers 2024 2024 2024
Graph neural news recommendation
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WebInteraction graph neural network for news recommendation. In Proceedings of the International Conference on Web Information Systems Engineering. Springer, 599 – 614. … 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.
WebACL Anthology - ACL Anthology WebOct 30, 2024 · In this paper, we propose to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics. The incorporated topic information would help indicate a user's interest and alleviate the sparsity of user-item interactions. Then we take advantage of graph neural networks to learn user and news representations ...
Webtations for news recommendation. However, it is not considered in many existing news rec-ommendation methods. In this paper, we pro-pose a neural news recommendation approach with topic-aware news representations. The core of our approach is a topic-aware news en-coder and a user encoder. In the news encoder we learn representations of … WebIn this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. 5 Paper Code …
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 …
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. snow bunting migrationWebJul 22, 2024 · Therefore, we propose an attention-based graph neural network news recommendation model. In our model, muti-channel convolutional neural network is used to generate news representations, and recurrent neural network is used to extract the news sequence information that users clicked on. snow bunting birds of the worldWebRecently, graph neural network (GNN) technology has been used more and more in recommender systems (Wu et al. 2024 ). The GNN-based recommendation model is … snow bunny too faced bronzerWebApr 14, 2024 · Thereby, we propose a new framework, dubbed Graph Neural Networks with Global Noise Filtering for Session-based Recommendation (GNN-GNF), aiming to filter noisy data and exploit items-transition ... snow bunting fun factsWebJan 25, 2024 · DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. snow bunting birds rangeWebMar 9, 2024 · Abstract. To extract finer-grained segment features from news and represent users accurately and exhaustively, this article develops a news recommendation (NR) … snow bunting bird faWebApr 14, 2024 · Recently, a technological trend has been to develop end-to-end Graph Neural Networks (GNNs)-based knowledge-aware recommendation (a.k.a., Knowledge Graph Recommendation, KGR) models. snow bunting bird picture