ArticleFuture generations computer systems : FGCS2021
Ontology based recommender system using social network data.
Article in Future generations computer systems : FGCS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Enhancing decision-making support by mining social media data with social network analysis.Social network analysis and mining · 2023Article
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Authors and funding
5 authors.
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Abstract
Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.