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From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Authors :
Tamim Mahmud Al-Hasan
Aya Nabil Sayed
Faycal Bensaali
Yassine Himeur
Iraklis Varlamis
George Dimitrakopoulos
Source :
Big Data and Cognitive Computing, Vol 8, Iss 4, p 36 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data sparsity problem. This survey paper presents an in-depth analysis of the paradigm shift from conventional recommender systems to generative pre-trained-transformers-(GPT)-based chatbots. We highlight recent developments that leverage the power of GPT to create interactive and personalized conversational agents. By exploring natural language processing (NLP) and deep learning techniques, we investigate how GPT models can better understand user preferences and provide context-aware recommendations. The paper further evaluates the advantages and limitations of GPT-based recommender systems, comparing their performance with traditional methods. Additionally, we discuss potential future directions, including the role of reinforcement learning in refining the personalization aspect of these systems.

Details

Language :
English
ISSN :
25042289
Volume :
8
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Big Data and Cognitive Computing
Publication Type :
Academic Journal
Accession number :
edsdoj.02ea8a238ee4d998499d61270fc3a18
Document Type :
article
Full Text :
https://doi.org/10.3390/bdcc8040036