Subin Lee

The success of many internet-based services that we use today, such as Youtube, Spotify, LinkedIn, Netflix, or Amazon, is driven by recommendation algorithms that allow users to enjoy content tailored to their preferences. Through my research, supervised by Professor Prasana Tambe in the Operations, Informations, and Decisions department, I plan to look into collaborative filtering algorithms that make recommendations based on similarities between users or items established through past behavior. There are a lot of complex models and different algorithms that are used to establish “similarity,” and I hope to delve deeply into them throughout the research process. While doing so, I also hope to become more proficient in Python and data analytics, storage, and processing––this will be done through online datasets such as the MovieLens Dataset or the BookCrossing Dataset. In addition to such technalities, I also plan to look into the implications of the technology, such as issues of cost, diversity of recommendations, algorithmic bias, data privacy, and advertisements. After completing my own projects, the goal will be to use this research to aid Professor Tambe in his research efforts.