Using Hypergraph-based User Profile in a Recommendation System

Tarakci, Hilal; Cicekli, Nihan Kesim

doi:10.5220/0005029600270035

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The context can be estimated a specific sort of learning which can be displayed as an ontology. An ontology-based setting models are powerful methods for sharing and combination of setting data [19,20]. A client setting ontological model can be created based on client's qualities, for example, individual data, aptitudes, inclinations and setting to give customized administrations [21].…”

Section: Usage Of Context and Ontologiesmentioning

confidence: 99%

Mobile Devices Interface Adaptivity Using Ontologies

Iqbal¹,

Naqvi²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Currently, many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces. The context offers the information base for the development of Adaptive user interface (AUI) frameworks to overcome the heterogeneity. For this purpose, the ontological modeling has been made for specific context and environment. This type of philosophy states to the relationship among elements (e.g., classes, relations, or capacities etc.) with understandable satisfied representation. The context mechanisms can be examined and understood by any machine or computational framework with these formal definitions expressed in Web ontology language (WOL)/Resource description frame work (RDF). The Protégé is used to create taxonomy in which system is framed based on four contexts such as user, device, task and environment. Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree. The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases. The semantic context model is focused to bring in the usage of adaptive environment. This exploration has finished up with a versatile, scalable and semantically verified context learning system. This model can be mapped to individual User interface (UI) display through smart calculations for versatile UIs.

show abstract

Section: Usage Of Context and Ontologiesmentioning

confidence: 99%

Mobile Devices Interface Adaptivity Using Ontologies

Iqbal¹,

Naqvi²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Hypergraphs relax this assumption of pairwise interaction and provide the freedom to model the interaction among k nodes. Such networks commonly occur in social networks 7 , 15 , metabolic networks 32 , recommender systems 17,28 and multiactor collaboration 25 .…”

Section: Introductionmentioning

confidence: 99%

Hyperedge Prediction Using Tensor Eigenvalue Decomposition

Maurya

Ravindran

2021

J Indian Inst Sci

View full text Add to dashboard Cite

show abstract

“…Hypergraphs relax this assumption of pairwise interaction and provide the freedom to model the interaction among k nodes. Such networks commonly occur in social networks [7,15], metabolic networks [32], recommender systems [17,28] and multi-actor collaboration [25].…”

Section: Introductionmentioning

confidence: 99%

Hyperedge Prediction using Tensor Eigenvalue Decomposition

Maurya¹,

Ravindran²

2021

Preprint

View full text Add to dashboard Cite

Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple dyadic interactions and could require the user to model super-dyadic associations among nodes. Such interactions can be modeled using a hypergraph, which is a generalization of a graph where a hyperedge can connect more than two nodes.In this work, we consider the problem of hyperedge prediction in a k−uniform hypergraph. We utilize the tensor-based representation of hypergraphs and propose a novel interpretation of the tensor eigenvectors. This is further used to propose a hyperedge prediction algorithm. The proposed algorithm utilizes the Fiedler eigenvector computed using tensor eigenvalue decomposition of hypergraph Laplacian. The Fiedler eigenvector is used to evaluate the construction cost of new hyperedges, which is further utilized to determine the most probable hyperedges to be constructed. The functioning and efficacy of the proposed method are illustrated using some example hypergraphs and a few real datasets. The code for the proposed method is available on this link.

show abstract

Using Hypergraph-based User Profile in a Recommendation System

Cited by 3 publications

References 0 publications

Mobile Devices Interface Adaptivity Using Ontologies

Mobile Devices Interface Adaptivity Using Ontologies

Hyperedge Prediction Using Tensor Eigenvalue Decomposition

Hyperedge Prediction using Tensor Eigenvalue Decomposition

Contact Info

Product

Resources

About