Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Data Science (MSDS)
Department
Computer Science
First Advisor
William Andreopoulos
Second Advisor
Genya Ishigaki
Third Advisor
Peter Gao
Keywords
Recommender systems, Graph Neural Networks, Textual embeddings, Ensemble learning, Evolving user preferences
Abstract
Recommender systems surround us. They shape what we watch, how we buy, and even what our future might look like next. The Amazon Review Dataset and Movielens, those two datasets will help this project explore how to improve recommender systems through the user’s preferences. Two methods were combined: sequence-based models and graph-based models. Sequence models, such as LSTMs and Transformers, look at the order of user actions to find patterns by their sequence. On the other hand, Graphbased models focus on relationships between users, items, and their attributes. Textual embeddings added depth and context. Both methods offer something special, according to metrics such as Precision@K and NDCG@K. They also offer even more scope for improving recommendations, with together offering them a further boost. This project highlights how combining time-based insights with Relational data provides more user experience.
Recommended Citation
Chen, Tianxiang, "Enhancing Recommender Systems Using Graph Neural Networks, Long Short-Term Memory and Textual Embeddings" (2025). Master's Projects. 1539.
DOI: https://doi.org/10.31979/etd.anxj-fu36
https://scholarworks.sjsu.edu/etd_projects/1539
