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Presenter: Atsuko Umeki
Supervisor:

Date: Tue, April 5, 2022
Time: 13:30:00 - 14:30:00
Place: ZOOM - Please see below.

ABSTRACT

https://uvic.zoom.us/j/87670289354?pwd=cWhrOEt4LzlhV2dCV1JlQW9ITG00QT09

Meeting ID: 876 7028 9354

Password: 757976

Abstract: Deep learning models have been used widely in various areas and applications of our everyday lives. They could also change the way non-profit organizations work
and help optimize fundraising results. In this thesis, sequential models are applied in fundraising to compare their performance against the traditional machine learning model. Sequential model is a type of neural network that is specialized for processing sequential data. Although some research utilizing machine learning algorithms in fundraising context exists, it is based on the data extracted from the specific time window, which does not take time-dependency of features into account; therefore, time-series features are independent at each data point relative to others. This approach results in loss of time notion. In this thesis, we experiment with the application of time-dependent sequential models including Long Short Term Memory
(LSTM), Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN) in the fundraising domain to predict the alumni monetary contribution to the university.
We also expand our study by including the architecture that treats time-invariant demographic data as a condition to the sequential layers. In this model, the time-dependent data is concatenated after running the sequential model. Our proposed models are empirically evaluated and compared against the traditional baseline models. As a result, the traditional model outperforms the proposed model; however, the proposed approach offers a viable alternative for pattern recognition of donor-giving behavior.