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Innovative project that was represented by our team at the Student Research Symposium as part of the First Symposium on Frontiers in Computer and Data Sciences that earned a Bronze Medal

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Student-Research-Symposium

Description

Gift shopping is tough when you don't know what the recipient likes, which can lead to regret. We tested whether Fashion Recommender Systems (FRS) are helpful when you have little information about someone's preferences. In our study, 192 pairs compared gift suggestions from FRS and people. Both methods were right over 50% of the time, even without talking to the gift-giver or recipient. Knowing the recipient personally did not help and made suggestions less accurate. We also noticed differences based on gender and used the scenario in a smartphone app. Our findings show the strengths and weaknesses of using FRS for choosing gifts with limited info.

How to Access

  1. Clone the repository to review the materials:

    git clone https://github.com/GiftRecsysLab/Student-Research-Symposium
  2. Explore the documentation and multimedia files contained within to gain a deeper understanding of the project’s contributions towards Fashion Recommender Systems.

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Innovative project that was represented by our team at the Student Research Symposium as part of the First Symposium on Frontiers in Computer and Data Sciences that earned a Bronze Medal

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