Designing a Novel Method for Personalizing Recommendations to Decrease Plastic Pollution
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Third world countries tend to have a higher share of plastic waste that is inadequately managed while the First world countries have higher plastic waste generation per person. A difference in the characteristics of plastic pollution depending on the country's standing results in varying optimal recommendations for users. Through Big Text and OSOME meme analysis, I constructed a list with optimal recommendations for First World and Third World countries. Based on the list, I designed a User Interface wit Google Apps Scripts that provide personalized recommendations based on the country's standing and user's preferred difficulty and reassessed the code based on the six qualities of code. The purpose of the User Interface is to aid people who wish to help solve plastic pollution by offering a set of personalized tasks for each user and keeping their progress accountable through a point tracking system. With a significant number of users, the application could eventually contribute to solving the problem of plastic pollution.
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