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