End-to-End Personalization via Unifying LLM Agents and Graph Attention Networks for Entertainment Recommendation
PhD. Seminar by: Danial Ebrat
Date: 17th March
Time: 2:30 PM
Location: EH186 (Essex Hall)
Abstract:
Recommender systems are central to helping users navigate the rapidly expanding entertainment ecosystem, yet achieving strong personalization with limited feedback while maintaining interpretability remains difficult, particularly under cold-start conditions and heterogeneous item metadata. This work presents an end-to-end hybrid recommendation framework that unifies Large Language Model (LLM) with Graph Attention Network (GAT)–based collaborative filtering to improve both ranking accuracy and explanation quality across movies, books, and music. LLM-based agents first transform raw metadata such as titles, genres, descriptions, and auxiliary attributes into semantically grounded user and item profiles, which are embedded and used as initial node features in a user–item bipartite graph processed by a GAT-based recommender. Model optimization relies on a hybrid objective combining Bayesian Personalized Ranking, cosine-similarity regularization, and robust negative sampling to better align semantic and collaborative signals. Finally, In the post-processing stage, an LLM-based agent reranks the GAT outputs using a proposed Hybrid Confidence-Weighted Binary Search Tree, and another LLM-based agent that produces natural-language justifications tailored to each user. Experiments on diverse benchmark datasets and extensive ablations demonstrate that the proposed methodology increases precision, recall, NDCG, and MAP across various K values. In addition, the post processing step is especially effective in cold-start scenarios, consistently strengthening recommendation metrics and enhancing transparency at smaller K values.
PhD Doctoral Committee:
Internal Reader: Dr. Jianguo Lu
Internal Reader: Dr. Ikjot Saini
External Reader: Dr. Esraa Abdelhalim
Advisor (s): Dr. Luis Rueda
Registration Link (MAC students Only)
