On Efficient Link Recommendation in Social Networks Using Actor-Fact Matrices

Ciesielczyk, Michal; Szwabe, Andrzej; Morzy, Mikołaj

doi:10.1155/2015/450215

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Context is the environmental factor that influences the expression of user needs [3]. Concerning the definition of contextual sensitivity, Akbulut et al explained that contextual sensitivity refers to the reactions and special needs of users in a given environment [4].…”

Section: Introductionmentioning

confidence: 99%

Research of Contextual Semantic Reasoning Model Based on Domain Ontology

Zhan

et al. 2021

Scientific Programming

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With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are divided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.

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Section: Introductionmentioning

confidence: 99%

Research of Contextual Semantic Reasoning Model Based on Domain Ontology

Zhan

et al. 2021

Scientific Programming

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Progressive Random Indexing

Ciesielczyk

Szwabe

Morzy

et al. 2017

ACM Trans. Internet Technol.

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The vector space model is undoubtedly among the most popular data representation models used in the processing of large networks. Unfortunately, the vector space model suffers from the so-called curse of dimensionality, a phenomenon where data become extremely sparse due to an exponential growth of the data space volume caused by a large number of dimensions. Thus, dimensionality reduction techniques are necessary to make large networks represented in the vector space model available for analysis and processing. Most dimensionality reduction techniques tend to focus on principal components present in the data, effectively disregarding local relationships that may exist between objects. This behavior is a significant drawback of current dimensionality reduction techniques, because these local relationships are crucial for maintaining high accuracy in many network analysis tasks, such as link prediction or community detection. To rectify the aforementioned drawback, we propose Progressive Random Indexing, a new dimensionality reduction technique. Built upon Reflective Random Indexing, our method significantly reduces the dimensionality of the vector space model while retaining all important local relationships between objects. The key element of the Progressive Random Indexing technique is the use of the gain value at each reflection step, which determines how much information about local relationships should be included in the space of reduced dimensionality. Our experiments indicate that when applied to large real-world networks (Facebook social network, MovieLens movie recommendations), Progressive Random Indexing outperforms state-of-the-art methods in link prediction tasks.

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On Efficient Link Recommendation in Social Networks Using Actor-Fact Matrices

Cited by 2 publications

References 18 publications

Research of Contextual Semantic Reasoning Model Based on Domain Ontology

Research of Contextual Semantic Reasoning Model Based on Domain Ontology

Progressive Random Indexing

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