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Hybrid recommendation system for beauty & spas salons in Yelp
- 1 School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom
- 2 Department of Materials, Imperial College London, London, SW7 2AZ, United Kingdom
* Author to whom correspondence should be addressed.
Abstract
The rising demand for beauty and healthy lifestyles makes the global beauty and spas salons market promising. How to choose appropriate salons for a specific customer becomes an issue and is underexplored. In this paper, we built two types of personalized recommendation system models, the pure model-based collaborative filtering model and the LightFM model (a kind of hybrid recommendation system model) to make recommendations for beauty and spas salons based on Yelp Dataset. The results showed the LightFM model had a better performance. Besides, by applying aspect-based sentimental analysis to extract new features from customer reviews, we further improved the performance of the LightFM model.
Keywords
hybrid recommendation system, aspect-based sentiment analysis, LightFM model
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Cite this article
Lin,Q.;Zhang,Y. (2023). Hybrid recommendation system for beauty & spas salons in Yelp. Applied and Computational Engineering,2,9-20.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
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