首页|CD-LLMCARS: Cross Domain Fine-Tuned Large Language Model for Context-Aware Recommender Systems

CD-LLMCARS: Cross Domain Fine-Tuned Large Language Model for Context-Aware Recommender Systems

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  • NETL
  • NSTL
  • IEEE
Recommender systems are essential for providing personalized content across various platforms. However, traditional systems often struggle with limited information, known as the cold start problem, and with accurately interpreting a user's comprehensive preferences, referred to as context. The proposed study, CD-LLMCARS (Cross-Domain fine-tuned Large Language Model for Context-Aware Recommender Systems), presents a novel approach to addressing these issues. CD-LLMCARS leverages the substantial capabilities of the Large Language Model (LLM) Llama 2. Fine-tuning Llama 2 with information from multiple domains can enhance the generation of contextually relevant recommendations that align with a user's preferences in areas such as movies, music, books, and CDs. Techniques such as Low-Rank Adaptation (LoRA) and Half Precision Training (FP16) are both effective and resource-efficient, allowing CD-LLMCARS to perform optimally in cold start scenarios. Extensive testing of CD-LLMCARS indicates outstanding accuracy, particularly in challenging scenarios characterized by limited user history data relevant to the cold start problem. CD-LLMCARS offers precise and pertinent recommendations to users, effectively mitigating the limitations of traditional recommender systems.

Recommender systemsLarge language modelsReviewsAccuracyHistoryContext modelingCollaborative filteringTuningTrainingUser experience

Adeel Ashraf Cheema、Muhammad Shahzad Sarfraz、Usman Habib、Qamar Uz Zaman、Ekkarat Boonchieng

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Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot, Pakistan

FAST School of Computing, National University of Computer and Emerging Sciences, Islamabad Campus, Islamabad, Pakistan

Department of Software Engineering, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot, Pakistan

Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand

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2025

10.1109/OJCS.2024.3509221

IEEE Open Journal of the Computer Society

IEEE Open Journal of the Computer Society

ISSN：
年,卷(期)：2025.6(1)
  • 51