Textual Influence Modeling Through Non-Negative Tensor Decomposition
2018
- 157Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage157
- Downloads99
- Abstract Views58
Thesis / Dissertation Description
No document is created in a vacuum. In all literature, there exists some influencing factor either in the form of cited documents, collaboration, or documents which authors have read. This influence can be seen within their works, and is present as a latent variable. This dissertation introduces a novel method for quantifying these influences and representing them in a semantically understandable fashion. The model is constructed by representing documents as tensors, decomposing them into a set of factors, and then searching the corpus factors for similarity.
Bibliographic Details
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