Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Clustering-Based Method for Automatic Educational Video Recommendation
  Using Deep Face-Features of Lecturers   Discovering and accessing specific content within educational video bases is
a challenging task, mainly because of the abundance of video content and its
diversity. Recommender systems are often used to enhance the ability to find
and select content. But, recommendation mechanisms, especially those based on
textual information, exhibit some limitations, such as being error-prone to
manually created keywords or due to imprecise speech recognition. This paper
presents a method for generating educational video recommendation using deep
face-features of lecturers without identifying them. More precisely, we use an
unsupervised face clustering mechanism to create relations among the videos
based on the lecturer's presence. Then, for a selected educational video taken
as a reference, we recommend the ones where the presence of the same lecturers
is detected. Moreover, we rank these recommended videos based on the amount of
time the referenced lecturers were present. For this task, we achieved a mAP
value of 99.165%.

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The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an