Neural Collaborative Filtering and Autoencoder Enabled Deep Learning Models for Recommender Systems

Arnold, Kwofie (2022-11-15)

Main Thesis


Finding important and useful information is getting harder as much more information is available online. The challenge for content producers is to deliver the appropriate content to the appropriate consumers while making it challenging for users to access that content. The foundation for overcoming these difficulties is provided by recommender systems. Traditional methods like Collaborative Filtering (CF) and Content-Based Recommender Systems have historically been successful in this field of study but are now challenged by problems with data sparsity, cold start, and non-linearity interaction. Evidently, several academic areas, like image detection and natural language processing (El-Bakry, 2008), have shown great interest in deep learning due to outstanding performance and the alluring quality of learning intricate representations. The impact of deep learning is recently showing good advancement when applied to recommender systems research (He, 2008). In this research We dive deep into the Autoencoder and Neural Collaborative Filtering based deep learning models and their implementation on classical collaborative filtering. The research also evaluates the performance of both models and outlines loopholes which can further be improved in future works.