Date of Award
2025
Document Type
Dissertation - SCU Access Only
Publisher
Santa Clara : Santa Clara University, 2025
Degree Name
Master of Science (MS)
Department
Computer Science and Engineering
First Advisor
Yi Fang
Abstract
Most research in recommender systems focuses on relevance, but diversity is equally important as it helps prevent filter bubbles and provides users with meaningful choices. This thesis explores the application of LoRA-enhanced LLMs for recommendation tasks across multiple datasets. While Large Language Models (LLMs) offer strong reasoning capabilities, standard LoRA-based fine-tuning often prioritizes personalization and relevance, struggling to maintain diversity and global quality in recommendations.
To address this, we propose two key enhancements:
(1) Personalized Adaptive Negative Sampling (PANS), dynamically balancing same-genre vs. different-genre exploration based on user engagement.
(2) Multi-Objective Loss Optimization, incorporating User Preference Score, Global Quality Score, and Genre-Based Entropy Loss to generate diverse and high-quality recommendations.
We further employ Bayesian Optimization for efficient hyperparameter tuning, ensuring faster convergence and better trade-offs between personalization, quality, and diversity. Extensive experiments on multiple datasets demonstrate the effectiveness of our approach in achieving a balance between recommendation accuracy and diversity.
Recommended Citation
Mo, Wei, "Beyond Relevance: A Multi-Objective Approach to Enhancing Diversity in LLM-Based Recommendations" (2025). Computer Science and Engineering Master's Theses. 46.
https://scholarcommons.scu.edu/cseng_mstr/46