Citation: Bok, K.; Song, J.; Lim, J.;
Yoo, J. Personalized Search Using
User Preferences on Social Media.
Electronics 2022, 11, 3049. https://
doi.org/10.3390/electronics11193049
Academic Editors:
Sławomir Nowaczyk, Rita P. Ribeiro
and Grzegorz Nalepa
Received: 9 August 2022
Accepted: 21 September 2022
Published: 24 September 2022
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Article
Personalized Search Using User Preferences on Social Media
Kyoungsoo Bok
1
, Jinwoo Song
2
, Jongtae Lim
2
and Jaesoo Yoo
2,
*
1
Department of Artificial Intelligence Convergence, Wonkwang University, Iksandae 460,
Iksan 54538, Jeonbuk, Korea
2
Department of Information and Communication Engineering, Chungbuk National University,
Chung-dae-ro 1, Seowon-Gu, Cheongju 28644, Chungbuk, Korea
* Correspondence: yjs@chungbuk.ac.kr; Tel.: +82-43-261-3230
Abstract:
In contrast to traditional web search, personalized search provides search results that
take into account the user’s preferences. However, the existing personalized search methods have
limitations in providing appropriate search results for the individual’s preferences, because they do
not consider the user’s recent preferences or the preferences of other users. In this paper, we propose a
new search method considering the user’s recent preferences and similar users’ preferences on social
media analysis. Since the user expresses personal opinions on social media, it is possible to grasp the
user preferences when analyzing the records of social media activities. The proposed method collects
user social activity records and determines keywords of interest using TF-IDF. Since user preferences
change continuously over time, we assign time weights to keywords of interest, giving many high
values to state-of-the-art user preferences. We identify users with similar preferences to extend the
search results to be provided to users because considering only user preferences in personalized
searches can provide narrow search results. The proposed method provides personalized search
results considering social characteristics by applying a ranking algorithm that considers similar user
preferences as well as user preferences. It is shown through various performance evaluations that the
proposed personalized search method outperforms the existing methods.
Keywords:
personalized search; user preference; activity information; similar user; ranking; social media
1. Introduction
With the advancement of the Internet and information systems, various data have
been generated and shared on the web, and information search has become increasingly
important [
1
,
2
]. When entering search terms into a web search engine, users hope that
the results that they want are shown at the top of the list, allowing them to quickly find
the desired web pages [
3
–
5
]. However, the traditional web search engine determines the
importance of each web document that contains the search term through ranking algorithms
to provide search results to users [
6
–
8
]. Therefore, most web search results do not provide
customized search results because they determine common results to all users [
9
–
12
]. This
problem arises because the method considers only the query form for the content-based
document search without accurately understanding the search intentions of web users.
That is, the traditional web searches do not provide search results suitable for the user’s
major preferences because they do not sufficiently reflect the user’s query intention [
13
–
16
].
When the term ‘Liverpool’ is entered for a web search, results for ‘Liverpool Football
Club’ and ‘Liverpool City’ are provided. In general, ‘Liverpool Football Club’ is at the top
of the list, and a large number of related webpages are displayed. Such results are provided
because the search engine seeks to show the results that users look for by considering
the ambiguity and lexical ambiguity of the word. Because users often click on web pages
associated with ‘Liverpool Football Club’, there are many web pages shown for it. Thus,
the need for personalized search that provides results according to the individual users’
preferences is increasing [
17
–
20
]. The personalized search can present the documents that
Electronics 2022, 11, 3049. https://doi.org/10.3390/electronics11193049 https://www.mdpi.com/journal/electronics
