Tagging Users’ Social Circles via Multiple Linear Regression
Abstract
:1. Introduction
2. Related Work
2.1. Social Circle
2.2. Tag Detection
3. Tag Detection of Social Circles
3.1. Overview
3.2. Features
- (1)
- Percentage of members who own the tagt is a tag and is the amount of members who own this tag.
- (2)
- The members’ average centrality who own the tagu is a circle member who owns the tag, is the number of this user’s friends in the circle.
- (3)
- The tag’s TF (Term Frequency) value in a circleIn our work, we regard the set of all members’ tags in a social circle as a document, and every tag in the set as a word of this document. For example, a user has two tags user:id:27 and school:id:10. The two items are words and all members’ words constitute the tag document of this social circle. Count(tag) is the amount of a tag item in the circle.
- (4)
- The tag’s IDF (Inverse Document Frequency) value in a circle
- (5)
- The tag’s TF-IDF value
- (6)
- If only one user owns the tagIf only one user owns this tag, is 1, otherwise, this is 0.
- (7)
- If only one social circle owns the tagIf only one social circle owns this tag, is 1, otherwise, is 0.
- (8)
- Prefix of the tagSome tags cannot be tags of social circles since they can only belong to a single user, such as user:id. We filter types of all tags and if a tag might be a social circle’s tag, is 1, otherwise, is 0.
3.3. Multiple Linear Regression
4. Experiment
4.1. Dataset
4.2. Baseline
4.3. Result Analysis
5. Conclusion
Acknowledgments
Author Contributions
Conflicts of Interest
References
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last_name, first_name, birthday, name, gender |
locale, hometown-name, hometown-id, education-school-name, education-school-id |
education-type, education-year-name, education-year-id, education-concentration-name |
education-concentration-id, id, location-name, location-id, education-classes-from-name |
education-classes-from-id, education-classes-with-name, education-classes-with-id |
education-classes-name education-classes-id, work-position-name work-position-id |
work-start_date, work-end_date work-employer-name, work-employer-id |
work-location-name, work-location-id, languages-name, languages-id |
middle_name, work-projects-name, work-projects-id, education-with-name |
education-with-id, work-projects-with-name, work-projects-with-id, work-description |
education-degree-name, education-degree-id, work-projects-start_date, work-with-name |
work-with-id, work-projects-from-name, work-projects-from-id |
education-classes-description, work-from-name, work-from-id, political, religion |
work-projects-end_date, work-projects-description, location |
Microsoft Academic Search | |||
Popular | P@10 | 28.29% | N/A |
FKE | P@10 | 12.01% | 15.08% |
TF-IDF | P@10 | 60.02% | 17.10% |
Multiple Linear Regression | P@10 | 71.54% | 40.63% |
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Qin, H.; Liu, J.; Lin, C.-Y.; Liu, T. Tagging Users’ Social Circles via Multiple Linear Regression. Informatics 2016, 3, 10. https://doi.org/10.3390/informatics3030010
Qin H, Liu J, Lin C-Y, Liu T. Tagging Users’ Social Circles via Multiple Linear Regression. Informatics. 2016; 3(3):10. https://doi.org/10.3390/informatics3030010
Chicago/Turabian StyleQin, Hailong, Jing Liu, Chin-Yew Lin, and Ting Liu. 2016. "Tagging Users’ Social Circles via Multiple Linear Regression" Informatics 3, no. 3: 10. https://doi.org/10.3390/informatics3030010