Alternative Title
CFGenNets
Abstract
Collaborative Filtering, a popular method for recommendation engines, models its predictions using past interactions between the entities in question (aka users/movies or customers/products etc). The method does not rely on the explicit properties of the entities, the identification of which may be intractable. In this work, we leverage this advantage rendered by Collaborative Filtering where the explicit features need not be defined apriori by evaluating its application to the domain of Ligand based Virtual Screening. We further attempt to address the drawback of Collaborative Filtering , ie the lack of interpret ability of the factors discovered through collaborative filtering by creating a novel class of generative deep learning models ,called Collaborative Filtering based Generative Networks (CFGenNets). We show the utility of CFGenNets in 2 domains 1) Ligand based Virtual Screening and 2) Image generation from keyword tags/meta descriptors.
Degree Date
Summer 7-29-2021
Document Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science and Engineering
Advisor
Eric C Larson
Subject Area
Computer Science
Number of Pages
163
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Srinivas, Raghuram, "Collaborative Filtering based Generative Networks" (2021). Computer Science and Engineering Theses and Dissertations. 19.
https://scholar.smu.edu/engineering_compsci_etds/19
