YPIE Scientist: Mircely Rodriguez

Research: Recommending People Potential Friends with Similar Interests using Vector Space Similarity Metrics

Mentor: Maury Courtland

Research Location: Yonkers, NY

Awards: Participant in Somers Science Fair (2024), Participant in Westchester Science and Engineering Fair (2025)

Abstract:

According to Heinrich and Gullone (2006), 70% of teenagers face recurring loneliness by age 18. This often stems from difficulties in making new friends with whom they feel a genuine connection. These challenges can be due to many reasons, such as feeling misunderstood, different energy levels, communication barriers, etc. Luckily, the internet has become a tool for meeting new people. While it allows individuals to easily meet people online, the question arises: how can individuals find potential friends with whom they would form a genuine connection? AI can be a valuable tool in solving this problem. Other researchers have attempted to address this issue by using collaborative filtering techniques to recommend their participants’ possible friends on social media. They used data from participants’ profiles on social networking sites and went based on that to recommend friends. This paper differs from that as it seeks to recommend participants’ potential friends in real life and specifically uses vector space similarity metrics, a type of collaborative filtering. In addition, the input for this experiment came directly from the participants themselves, which ensured more personalized recommendations to each individual. This approach conducted a self-report 10-point Likert scale survey with 15 generalized recreational activities (n = 30 respondents). The data was used to calculate Euclidean distance between participants’ responses. Each respondent was informed of their nearest vector and farthest vector. They were then set up to have a conversation and both completed a post-conversation survey. On that survey, individuals completed another self-report 10-point Likert scale survey assessing the conversations they had. This survey asked questions like “Did you feel comfortable?​”, “Was there a connection?”, “Would you meet again?”, “Do you see yourself becoming friends with this person?”. A linear regression model was fit to the data modeling the relationship between Euclidean distance and the number that individuals rated on the post-conversation survey for “Do you see yourself becoming friends with this person?”. When modeled, there was no relationship between these two variables (slope = 0.00609552316). This disproves the hypothesis that a lower Euclidean distance increases likeliness of individuals becoming friends. In conclusion, vector space similarity metrics was useful in uniting individuals with similar interests but not for making new friends.

About this Scientist:   

Mircely Rodriguez is a junior who is currently pursuing her IB Diploma at Yonkers High School. She is interested in learning new things, taking on academic challenges, and making a difference in people’s lives. When she goes to college she plans to major in speech pathology to help others communicate more effectively and confidently.

Presentation Slides