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Review

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

by
Tamim Mahmud Al-Hasan
1,
Aya Nabil Sayed
1,
Faycal Bensaali
1,*,
Yassine Himeur
2,
Iraklis Varlamis
3 and
George Dimitrakopoulos
3
1
Department of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
2
College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates
3
Department of Informatics and Telematics, Harokopio University of Athens, GR-17778 Athens, Greece
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2024, 8(4), 36; https://doi.org/10.3390/bdcc8040036
Submission received: 8 February 2024 / Revised: 16 March 2024 / Accepted: 21 March 2024 / Published: 27 March 2024
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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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.
Keywords: conversational recommender systems; context-aware recommenders; generative pre-trained transformer (GPT); hybrid recommenders; natural language processing (NLP) conversational recommender systems; context-aware recommenders; generative pre-trained transformer (GPT); hybrid recommenders; natural language processing (NLP)

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MDPI and ACS Style

Al-Hasan, T.M.; Sayed, A.N.; Bensaali, F.; Himeur, Y.; Varlamis, I.; Dimitrakopoulos, G. From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions. Big Data Cogn. Comput. 2024, 8, 36. https://doi.org/10.3390/bdcc8040036

AMA Style

Al-Hasan TM, Sayed AN, Bensaali F, Himeur Y, Varlamis I, Dimitrakopoulos G. From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions. Big Data and Cognitive Computing. 2024; 8(4):36. https://doi.org/10.3390/bdcc8040036

Chicago/Turabian Style

Al-Hasan, Tamim Mahmud, Aya Nabil Sayed, Faycal Bensaali, Yassine Himeur, Iraklis Varlamis, and George Dimitrakopoulos. 2024. "From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions" Big Data and Cognitive Computing 8, no. 4: 36. https://doi.org/10.3390/bdcc8040036

APA Style

Al-Hasan, T. M., Sayed, A. N., Bensaali, F., Himeur, Y., Varlamis, I., & Dimitrakopoulos, G. (2024). From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions. Big Data and Cognitive Computing, 8(4), 36. https://doi.org/10.3390/bdcc8040036

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