Is the suggested food your desired?: Multi-modal recipe recommendation with demand-based knowledge graph

Lei, Zhenfeng; Haq, Anwar Ul; Zeb, Adnan; Suzauddola,; Zhang, Defu

doi:10.1016/j.eswa.2021.115708

Cited by 26 publications

(6 citation statements)

References 33 publications

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“…In order to figure out the basic rationale of a user-item interaction, KPRN 14 conducted knowledge reasoning on paths by leveraging the sequential dependencies within paths connected to users and items and designed a weighted operation for path distribution. Zhang et al 15 used graph neural networks for feature extraction of nodes in the graph, for the purpose of preserving higher-order neighborhood information and achieving node-level and graph-level representation of the knowledge graph. Besides, KGCN 16 and KGAT 27 focused on the graph structure and employed the attention mechanism in neighbors to mine associated attributes of each node.…”

Section: Related Workmentioning

confidence: 99%

Iterative heterogeneous graph learning for knowledge graph-based recommendation

Liu

Shen

Chang

et al. 2023

Sci Rep

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Incorporating knowledge graphs into recommendation systems has attracted wide attention in various fields recently. A Knowledge graph contains abundant information with multi-type relations among multi-type nodes. The heterogeneous structure reveals not only the connectivity but also the complementarity between the nodes within a KG, which helps to capture the signal of potential interest of the user. However, existing research works have limited abilities in dealing with the heterogeneous nature of knowledge graphs, resulting in suboptimal recommendation results. In this paper, we propose a new recommendation method based on iterative heterogeneous graph learning on knowledge graphs (HGKR). By treating a knowledge graph as a heterogeneous graph, HGKR achieves more fine-grained modeling of knowledge graphs for recommendation. Specifically, we incorporate the graph neural networks into the message passing and aggregating of entities within a knowledge graph both at the graph and the semantic level. Furthermore, we designed a knowledge–perceiving item filter based on an attention mechanism to capture the user’s potential interest in their historical preferences for the enhancement of recommendation. Extensive experiments conducted on two datasets in the context of two recommendations reveal the excellence of our proposed method, which outperforms other benchmark models.

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Section: Related Workmentioning

confidence: 99%

Iterative heterogeneous graph learning for knowledge graph-based recommendation

Liu

Shen

Chang

et al. 2023

Sci Rep

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“…further introduce social relationships into the food knowledge graph. 72 They construct a multimodal and hierarchical recipe knowledge graph (RcpKG). In RcpKG, the users’ demands are converted to nodes and modeled with specific hierarchical structures.…”

Section: Development Of the Food Knowledge Graphmentioning

confidence: 99%

Applications of knowledge graphs for food science and industry

Min

et al. 2022

Patterns

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“…It is reasonable to destroy correct triples (s, p, o) ∈ S by replacing entities, and construct incorrect triples (s ′ , p ′ , o) ∈ S ′ . When breaking triples, we follow [30][31][32][33][34][35][36][37][38][39][40][41] and assign different probabilities to head/tail entity replacement.…”

Section: Training Targetmentioning

confidence: 99%

TBRm: A Time Representation Method for Industrial Knowledge Graph

Cao

Zheng

2022

Applied Sciences

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With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things (IoT) have empowered modern industries to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). Using knowledge graphs (KG) to process data from the Industrial Internet of Things (IIoT) is a research field worthy of attention, but most of the researched knowledge graph technologies are mainly concentrated in the field of static knowledge graphs, which are composed of triples. In fact, many graphs also contain some dynamic information, such as time changes at points and time changes at edges; such knowledge graphs are called Temporal Knowledge Graphs (TKGs). We consider the temporal knowledge graph based on the projection and change of space. In order to combine the temporal information, we propose a new representation of the temporal knowledge graph, namely TBRm, which increases the temporal dimension of the translational distance model and utilizes relational predicates in time add representation in time dimension. We evaluate the proposed method on knowledge graph completion tasks using four benchmark datasets. Experiments demonstrate the effectiveness of TBRm representation in the temporal dimension. At the same time, it is also practiced on a network security data set of the Industrial Internet of Things. The practical results prove that the TBRm method can achieve good performance in terms of the degree of harm to IIoT network security.

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Is the suggested food your desired?: Multi-modal recipe recommendation with demand-based knowledge graph

Cited by 26 publications

References 33 publications

Iterative heterogeneous graph learning for knowledge graph-based recommendation

Iterative heterogeneous graph learning for knowledge graph-based recommendation

Applications of knowledge graphs for food science and industry

TBRm: A Time Representation Method for Industrial Knowledge Graph

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