| Title |
Hoggan, Ashlee_MCS_2021 |
| Alternative Title |
Gender Prediction Based on Food Selection |
| Creator |
Hoggan, Ashlee |
| Collection Name |
Master of Computer Science |
| Description |
The following Master of Science of Computer Science thesis explores gendered stereotypes in food by examining participant's answers while allowing machine learning algorithms to determine whether the individual is male or female. |
| Abstract |
The research presented in this paper investigates whether an individual's gender can be predicted based on their recipe preferences. Gender is commonly used in demographic recommendation engines to improve CTR (click-through rates). Previous research has found that there are gendered stereotypes in food, and those stereotypes would classify desserts as feminine and hearty meals like steaks as masculine. A more in depth look into this subject is examined in this thesis by having participants answer a series of 200 questions that determine whether there is a gender stereotype phenomenon within recipe selections and whether the questions' results can allow machine learning algorithms to determine whether an individual is male or female. The results of this study found that the machine learning algorithms used for testing only had a 50% accuracy rate when determining someone's gender based on their recipe selections. Although the machine learning algorithms only had a 50% accuracy rate, there was statistical significance with the meat versus dessert testing category. However, I did find that men are more likely to choose meat recipes over dessert recipes, and women are more likely to select dessert recipes over meat recipes. |
| Subject |
Algorithms; Gender; Computer science |
| Keywords |
Demographic recommendation engines; gender; machine-learning algorithms; recipes |
| Digital Publisher |
Stewart Library, Weber State University, Ogden, Utah, United States of America |
| Date |
2021 |
| Medium |
Thesis |
| Type |
Text |
| Access Extent |
1.35 MB; 70 page PDF |
| Language |
eng |
| Rights |
The author has granted Weber State University Archives a limited, non-exclusive, royalty-free license to reproduce their theses, in whole or in part, in electronic or paper form and to make it available to the general public at no charge. The author retains all other rights. |
| Source |
University Archives Electronic Records; Master of Computer Science. Stewart Library, Weber State University |
| Format |
application/pdf |
| ARK |
ark:/87278/s68nmvz9 |
| Setname |
wsu_smt |
| ID |
96849 |
| Reference URL |
https://digital.weber.edu/ark:/87278/s68nmvz9 |
