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168538 https://hdl.handle.net/2286/R.2.N.168538 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2022 63 pages Masters Thesis Academic theses Text eng Shah, Aksheshkumar Ajaykumar Venkateswara, Hemanth Berman, Spring Ladani, Leila J Arizona State University Partial requirement for: M.S., Arizona State University, 2022 Field of study: Mechanical Engineering Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. This thesis introduces a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based Recommendation. This system models user purchase behavior using weighted user-product preferences (explicit feedback) rather than binary user-product interactions (implicit feedback). Using this a novel sparse adversarial model, Sparse ReguLarized Generative Adversarial Network (SRLGAN), is developed for Cold-Start Recommendation. SRLGAN leverages the sparse user-purchase behavior which ensures training stability and avoids over-fitting on warm users. The performance of SRLGAN is evaluated on two popular datasets and demonstrate state-of-the-art results. Computer Science Artificial Intelligence Statistics Adversarial Machine Learning Cold-Start Recommendation Generative Adversarial Networks (GANs) Machine learning Recommendation Systems Sparsity Adversarial Machine Learning for Recommendation Systems