User Interest Modeling from Social Media Network Graph, Enriched with Semantic Web

Shah, Bansari; Verma, Ananda Prakash; Tiwari, Shobhit

doi:10.1007/978-981-10-6319-0_5

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…In [35], the authors proposed a user interest modeling approach in micro-blog system based on multi-tag semantic correlation via analyzing tag relationship and the limitations of the existing micro-blog user interest models. In [36], the user interest is modeled by users' activities on social media to exploit the change in one's behaviors over a period by following users' activities on social media over time.…”

Section: User Interest Modelingmentioning

confidence: 99%

Product Adoption Maximization Leveraging Social Influence and User Interest Mining

Ji¹,

Huang²,

Liu³

et al. 2021

KSII TIIS

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A Social Networking Service (SNS) platform provides digital footprints to discover users' interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users' relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users' interest and social influence, where the user interest model relies on historical user behavior and the seed users' evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users' past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

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Section: User Interest Modelingmentioning

confidence: 99%

Product Adoption Maximization Leveraging Social Influence and User Interest Mining

Ji¹,

Huang²,

Liu³

et al. 2021

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Kapanipathi et al (2014) process a hierarchy on 'Wikipedia Concept Graph (WCG)' to come up with 'User Interest Generator' and 'Interest Hierarchy Generator', mapping user's primitive interests to Wikipedia hierarchy. Shah et al (2018) propose enrichment technique using DBpedia Ontology, generating niche interest and inferred general interest and works for least active users as well. Interests identified using DBpedia aggregates into a user profiling framework in (Orlandi et al, 2012).…”

Section: Literature Surveymentioning

confidence: 99%

A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets

Maravanthe¹,

Rao²,

Kanavalli³

et al. 2020

Journal of Computer Science

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The past decade has seen a tremendous increase in the adoption of Social Web leading to the generation of enormous amount of user data every day. The constant stream of tweets with an innate complex sentimental and contextual nature makes searching for relevant information a herculean task. Multiple applications use Twitter for various domain sensitive and analytical use-cases. This paper proposes a scalable context modeling framework for a set of tweets for finding two forms of metadata termed as primary and extended contexts. Further, our work presents a hierarchical clustering approach to find hidden patterns by using generated primary and extended contexts. Ontologies from DBpedia are used for generating primary contexts and subsequently to find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology forms the backbone for this proposed model. We consider both twitter trend and stream data to demonstrate the application of these contextual parts of information appropriate in clustering. We also discuss the advantages of using hierarchical clustering and information obtained from cutting dendrograms.

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User behavior mining on social media: a systematic literature review

Safari

Rahmani

Alizadeh

2019

Multimed Tools Appl

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User Interest Modeling from Social Media Network Graph, Enriched with Semantic Web

Cited by 6 publications

References 11 publications

Product Adoption Maximization Leveraging Social Influence and User Interest Mining

Product Adoption Maximization Leveraging Social Influence and User Interest Mining

A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets

User behavior mining on social media: a systematic literature review

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