Summary of A Natural Language Processing Framework For Hotel Recommendation Based on Users’ Text Reviews, by Lavrentia Aravani et al.
A Natural Language Processing Framework for Hotel Recommendation Based on Users’ Text Reviews
by Lavrentia Aravani, Emmanuel Pintelas, Christos Pierrakeas, Panagiotis Pintelas
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel application of Deep Learning algorithms, particularly Natural Language processing models, is proposed for hotel recommendation systems. By extracting semantic knowledge from text reviews, these models can create more efficient recommendation systems that classify users’ preferences and emotions. This study presents a Natural Language Processing framework utilizing customer text reviews to provide personalized recommendations based on preferences. The framework leverages Bidirectional Encoder Representations from Transformers (BERT) and a fine-tuning/validation pipeline categorizing texts into “Bad,” “Good,” or “Excellent” recommended hotels. Results demonstrate that the proposed system can significantly enhance user experience by providing personalized recommendations based on user preferences and previous booking history. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re looking for a hotel, but you want one that fits your style perfectly. This paper shows how to use special AI algorithms called Deep Learning to make hotel recommendation systems more personal. It’s like having a friend who knows what you like and can suggest the best place for you. The system uses natural language processing, which is like understanding human language, to analyze customer reviews and find patterns that help predict what kind of hotel someone will like. This can lead to better recommendations that make booking hotels easier and more enjoyable. |
Keywords
» Artificial intelligence » Bert » Deep learning » Encoder » Fine tuning » Natural language processing
