The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media Article Swipe
Marwa M. Alrehili
,
Wael M. S. Yafooz
,
Abdullah Alsaeedi
,
Abdel-Hamid M. Emara
,
Aldosary Saad
,
Hussain Al-Aqrabi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12042157
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12042157
The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ implicit interests. This paper is aims to propose a novel user interest mining approach and model based on demographic data, big five personality traits and similarity between the topics based on clusters. To demonstrate the leverage of combining user personality traits and demographic data into interest investigation, various experiments were conducted on the collected data. The experimental results showed that looking at personality and demographic data gives more accurate results in mining systems, increases utility, and can help address cold start problems for new users. Moreover, the results also showed that interesting topics were the dominant factor. On the other hand, the results showed that the current users’ implicit interests can be predicted through the cluster based on similar topics. Moreover, the hybrid model based on graphs facilitates the study of the patterns of interaction between users and topics. This model can be beneficial for researchers, people on social media, and for certain research in related fields.
Related Topics
Concepts
Computer science
Social media
Leverage (statistics)
Personality
Personalization
Collaborative filtering
Big Five personality traits
Similarity (geometry)
Data science
World Wide Web
User-generated content
Recommender system
Internet privacy
Artificial intelligence
Psychology
Social psychology
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app12042157
- https://www.mdpi.com/2076-3417/12/4/2157/pdf?version=1645604657
- OA Status
- gold
- Cited By
- 3
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213155680
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213155680Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app12042157Digital Object Identifier
- Title
-
The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social MediaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-18Full publication date if available
- Authors
-
Marwa M. Alrehili, Wael M. S. Yafooz, Abdullah Alsaeedi, Abdel-Hamid M. Emara, Aldosary Saad, Hussain Al-AqrabiList of authors in order
- Landing page
-
https://doi.org/10.3390/app12042157Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/12/4/2157/pdf?version=1645604657Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/12/4/2157/pdf?version=1645604657Direct OA link when available
- Concepts
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Computer science, Social media, Leverage (statistics), Personality, Personalization, Collaborative filtering, Big Five personality traits, Similarity (geometry), Data science, World Wide Web, User-generated content, Recommender system, Internet privacy, Artificial intelligence, Psychology, Social psychology, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3Per-year citation counts (last 5 years)
- References (count)
-
61Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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