Forming a TED talks sphere for convenient search

Published date29 April 2020
DOIhttps://doi.org/10.1108/EL-10-2019-0238
Pages403-420
Date29 April 2020
AuthorChia-Hui Shih,Han-Lin Li,Chih-Chien Hu,Bertrand M.T. Lin
Forming a TED talks sphere for
convenient search
Chia-Hui Shih and Han-Lin Li
Institute of Information Management, National Chiao Tung University,
Hsinchu, Taiwan
Chih-Chien Hu
Bachelor Program in Interdisciplinary Studies, College of Future,
National Yunlin University of Science and Technology, Yunlin, Taiwan, and
Bertrand M.T. Lin
Institute of Information Management, National Chiao Tung University,
Hsinchu, Taiwan
Abstract
Purpose TED (Technology, Entertainment, Design, www.ted.com/) Talks has been one of the most
popular video systems. However, the current TED Talks system expressed its inquired videosas in a two-
dimensional (2D) table, which is inconvenient for searching the relationships among videos and tags. This
study converts the TED Talks table into a sphereby using optimization techniques to help users search for
preferredvideos.
Design/methodology/approach There are ve phases in this study as follows.Phase 1: Reorganize
data of 36 tags and108 videos; Phase 2: Allocate tags on the TED sphere;Phase 3: Allocate videos on the TED
sphere; Phase 4: Develop an online interactive TED retrieval system; and Phase 5: Perform survey and
evaluation.
Findings One survey demonstrated that theTED Talks sphere is more convenient for searching videos,
as it is more user-friendly because of its graphical user interface, more convenient to use, more useful for
retrievinginformation and can facilitate a more responsive search for userspreferredvideos.
Research limitations/implications The numbers oftags and videos able to be displayed on a sphere
is limited by the capacityof an optimizationsoftware and hardware.
Practical implications The proposed sphere system can be usedby a large number of users of TED
Talks groups.
Social implications This sphere systems can also be applied to other elds which use 2D forms to
display the relationshipsamong objects.
Originality/value This study uses an optimization method to convert a 2D form into a 3D sphere to
highlightthe relationships among numerous objects.
Keywords Optimization, TED Talks, Visualization, 3D sphere, Retrieval systems, Databases
Paper type Research paper
1. Introduction
Information visualization is a presentation technique to display information in a suitable
format which provides users with concepts to interact and explore knowledge, enabling
The authors would like to thank the National Chiao Tung University, Grant Number 108W248, and
the Ministry of Science and Technology, Taiwan, R.O.C., Grant Number MOST 107-2221-E-009-104
for funding this research.
TED Talks
sphere
403
Received14 October 2019
Revised13 February 2020
Accepted17 March 2020
TheElectronic Library
Vol.38 No. 2, 2020
pp. 403-420
© Emerald Publishing Limited
0264-0473
DOI 10.1108/EL-10-2019-0238
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0264-0473.htm
users to nd an interesting pattern (Dzemyda et al., 2013;Ivanikovas et al.,2008;
Leeuwenberg, 2019;Seo and Shneiderman, 2005;Shneiderman, 2003;Subirats et al.,2019).
Recently, the number of needs for information visualization for users to interact with
knowledge in an accessiblegraphical form is growing tremendously.
One of the internet video content providers, TED (Technology, Entertainment, Design,
www.ted.com/) Talks, only provides linear text lists (i.e. table form) to display the search
results of videos to users. Users shouldidentify the keywords with their queries to examine
the search results on webpages. It cannotperform an overall view of videos by usersquery
keywords. This kind of presentation structureis time-consuming when users get redirected
to a large number of videos, and it is difcult to help users identify the relationshipsamong
videos.
Several studies dened visualizing decision methods. They try to bridge the gaps
between decision-making and multidimensional data visualizations to support users in
visualizing objects (Dzemyda et al.,2013;Köpp and Weinkauf, 2018;Li and Ma, 2008). One
of the visual pieces of information with hierarchical information structures contain
structural information and content information (Johnson and Shneiderman, 1991). Treemap
is a visualization technique that depicts the structure and content of multiple dimensions
data based on the hierarchical structure(Görtler et al., 2018;Johnson and Shneiderman, 1991;
Köpp and Weinkauf, 2018). The hierarchical structure can effectively group the multiple
dimensions data based on their attributes, but it is difcult to perform the relations among
content objects allocatedin different groups or hierarchical levels intuitively.
As the information visualization methodsof linear list and treemap used for TED Talks
are ineffective in performing the context between videos, their structures are unable to
determine an overall scope of meaningful and relevant videos (Belém et al., 2014;Huand Li,
2017). Hu and Li (2017) proposed a navigation map to effectively show the relationships
among TED videos. However, their navigation map is inefcient to perform context
relations among a huge number of TED videos, as the navigation map was used with non-
linear optimizationmodels.
Therefore, the goal of the study is to propose a spherical system by using integer
programming to establish the relationships among TED videos on a sphere. More than 30
tags and 100 videos on the visual surface of the sphere can be assigned meaningfully and
effectively and then be retrieved visually. The video-tag sphere was generated by usingan
optimization model and solved by using LINGO (www.lindo.com/index.php/products/lingo-
and-optimization-modeling).
2. Related works
Visualization of multidimensional data is highly important in data mining because a large
amount of data needs specic means for the knowledge of discovery (Dzemyda et al.,2013;
Ivanikovas et al., 2008). One of the ways to visualize multidimensional data is to project it
onto a plane.
Previous investigations show that it is possible to train machine learning models to
determine a visualization of multidimensional data (Dzemyda et al.,2013). Ivanikovas et al.
(2008) presented an unsupervised backpropagation algorithm to train a multilayer feedforward
neural network to perform a projection for visualizing information. This allowed the speeding
up of the visualization of clustering large datasets among varied groups.
Besides that, several two-dimensional (2D) plane visualization methods were used to depict
multidimensional data (Chuang et al., 2012;Dzemyda et al., 2013;Hu and Li, 2017;Johnson and
Shneiderman, 1991;Köpp and Weinkauf, 2018). Chuang et al. (2012) demonstrated a visual
analysis tool using a tabular layout for assessing textual topics. Their method provided a
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