Analysis of Relationship Between Personality and Favorite Places with Poisson Regression Analysis

Song, Ha Yoon; Kang, Hwa Baek

doi:10.1051/itmconf/20181602001

Cited by 5 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the ranked data, the degree of the relationship between personality and location preference was observed to be 43%. As opposed to other researches Song and Lee [4]; Draper and Smith [5]; Song and Kang [6] which deal with the direct relationship between personality and specific locations or location categories, our research shows the degree of the relationship. The count of the location data seems to be sufficient to apply regression analysis, while we are doubtful of the count of the personality data, which is provided by 30 volunteers.…”

Section: Discussioncontrasting

confidence: 90%

“…Similar to the methods in Song and Lee [4], regression analysis was applied. Also, Song and Kang analyzed the relationship between personality and favorable places, with personality as an independent variable and locations as the dependent variable, using various regression methods such as Poisson regression, ZINB regression, and Quantile regression [6]. Results Amichai-Hamburger and Vinitzky are also based on human personality and its relationship with social networks [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Analysis of Correlation between Personality and Visiting Place using Spearman’s Rank Correlation Coefficient

Song¹,

Park²

2020

KSII TIIS

View full text Add to dashboard Cite

Recent advancements in mobile device technology have enabled real-time positioning so that mobile patterns of people and favorable locations can be identified and related researches have become plentiful. One of the fields of research is the relationship between the object properties and the favored location to visit. The object properties of a person include personality, which is a major property jobs, income, gender, and age. In this study, we analyzed the relationship between the human personality and the preference of the location to visit. We used Spearman's Rank correlation coefficient, one of the many methods that can be used to determine the correlation between two variables. Instead of using actual data values, Spearman's Rank correlation coefficient deals with the ranks of the two data sets. In our research, the personality and the location data sets are used. Our personality data is ranked in five ranks and the location data is ranked in 8 ranks. Spearman's Rank correlation coefficient showed better results compared to Pearson linear correlation coefficient and Kendall rank correlation coefficient. Using Spearman's correlation coefficient, the degree of the relationship between the personality and the location preference is found to be 43%.

show abstract

Section: Discussioncontrasting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

An Analysis of Correlation between Personality and Visiting Place using Spearman’s Rank Correlation Coefficient

Song¹,

Park²

2020

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…SWARM is an application that records the location of a visit when a user visits a site. Location data was created by categorizing each visit data to ten industry classification and accumulating number of visits for each category [14] [15]. Ten Industry categories include Foreign Institutions, Retail, Service industry, etc.…”

Section: Location Categoriesmentioning

confidence: 99%

Analysis of the Correlation Between Personal Factors and Visiting Locations With Boosting Technique

Song

Yun

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

The paper analyzed the relationship between the person's fourteen characteristic factors and place to visit. The personal factors consist of personality, marital Status, final education, majors, religion, monthly income, commuting means and time, frequency of travel, userage of social media, time spent on social media per day, cultural type. In addition, the analysis was done on which factors have the greatest impact. The analysis involved thirty-four participants and the boosting technique was used as a method of analysis.

show abstract

“…Coefficient of Determination is used in the relation between personality and favored locations [1]. By use of probability models such as Poisson distribution, the relation between personality and favored locations are identified and personal mobility model is predicted in [2,3]. These are traditional methods of analysis based on statistics.…”

Section: Introductionmentioning

confidence: 99%

Finding Location Visiting Preference from Personal Features with Ensemble Machine Learning Techniques and Hyperparameter Optimization

Kim¹,

Song

2021

Applied Sciences

View full text Add to dashboard Cite

For the question regarding the relationship between personal factors and location selection, many researches support the effect of personal features for personal location favorite. However, it is also found that not all of personal factors are effective for location selection. In this research, only distinguished personal features excluding meaningless features are used in order to predict visiting ratio of specific location categories by using three different machine learning techniques: Random Forest, XGBoost, and Stacking. Through our research, the accuracy of prediction of visiting ratio to a specific location regarding personal features are analyzed. Personal features and visited location data had been collected by tens of volunteers for this research. Different machine learning methods showed very similar tendency in prediction accuracy. As well, precision of prediction is improved by application of hyperparameter optimization which is a part of AutoML. Applications such as location based service can utilize our result in a way of location recommendation and so on.

show abstract

Analysis of Relationship Between Personality and Favorite Places with Poisson Regression Analysis

Cited by 5 publications

References 4 publications

An Analysis of Correlation between Personality and Visiting Place using Spearman’s Rank Correlation Coefficient

An Analysis of Correlation between Personality and Visiting Place using Spearman’s Rank Correlation Coefficient

Analysis of the Correlation Between Personal Factors and Visiting Locations With Boosting Technique

Finding Location Visiting Preference from Personal Features with Ensemble Machine Learning Techniques and Hyperparameter Optimization

Contact Info

Product

Resources

About