Conversational Recommender System Using a Combination of Fine-Tuned GPT-4o and Retrieval-Augmented Generation for Laptop Recommendations - Dalam bentuk buku karya ilmiah

FATHAN ASKAR

Informasi Umum

Kode

25.04.470

Klasifikasi

000 - General Works

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Recommender Systems

Dilihat

113 kali

Informasi Lainnya

Abstraksi

Conversational recommender systems (CRS) have revolutionized personalized recommendations in recommender systems by using interactive and adaptive decision-making, particularly in complex domains (e.g., laptops). Existing CRS provides interaction between the system and the user through Form-based Layouts and Natural Language. Natural language- based interactions are typically constructed using Conventional Natural Language Processing (C-NLP) methods. While both interactions have shown certain successes, they also have limitations. Form-based layouts restrict users from expressing their preferences freely because of their rigid and structured nature. On the other hand, C-NLP allows for more dynamic interactions but relies heavily on domain-specific datasets and still struggles to interpret complex user requirements. To tackle these issues, we propose the development of a CRS using Large Language Models (LLMs). Specifically, we combined a Fine-Tuned GPT-4o model and the retrieval technique of Retrieval-Augmente