Cross-attention fusion connects the two tasks to learn the feature interaction between the item vector in the recommendation task and the entity vector in the knowledge graph.
Jan 13, 2022
We propose a knowledge graph embedding model with attention-based high-low level feature interaction convolutional network. ... A criss-cross attention mechanism ...
In this section, we review the necessary knowledge related to the research of this paper, which are graph convolutional network, deep clustering and ...
Missing: recommendation. | Show results with:recommendation.
Dec 27, 2023 · Yu, “Improving conversational recommender systems via knowledge graph based semantic fusion,” in Proc. of KDD. ACM, 2020, pp. 1006–1014 ...
Mar 1, 2024 · In addressing the aforementioned challenges, this paper introduces a novel approach utilizing a graph neural network. To overcome the lack of ...
Feature Similarity Learning Enhanced Knowledge Graph-based ... - Scilit
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Keywords · Cross attention fusion for knowledge graph optimized recommendation · Research on Personalized Recommendation Algorithm Based on Knowledge Graph and ...
Apr 28, 2023 · In this paper, we propose a new recommendation method based on iterative heterogeneous graph learning on knowledge graphs (HGKR). By treating a ...
A multi-scale cross-attention fusion network (MCFN) is proposed to achieve total extraction and compelling fusion of feature information at different scales and ...
Jun 29, 2024 · We develop a knowledge graph convolutional network that encodes semantic and structural information in both the knowledge graph and the user- ...
A curated list of AWESOME papers, datasets and tutorials within Multimodal Knowledge Graph. - awesome-multimodal-knowledge-graph/resource_list_abstract.md ...