The online recommendation system has benefited the traditional restaurant business economically. However, finding the best restaurant during rush time and visiting new places is tough. This objective is addressed through a restaurant recommendation approach, which impacts the human decision-making method. With the help of collaborative filtering, some user-based recommendation systems were designed to generate the best recommendation based on user choices. Thus, a user preferences-based method is presented using A Lite Bidirectional Encoder Representations from Transformers and Simple Recurrent Unit to suggest restaurants based on user preferences. Here, a publicly available dataset from Kaggle called Kzomato is used with 9552 samples and 21 features. And the system obtained an F1-score, precision, and recall of 86%, which will save time and provide the best recommendation based on user preferences easily.
CITATION STYLE
Keya, A. J., Arpona, S. A., Kabir, M. M., & Mridha, M. F. (2023). Recurrent ALBERT for recommendation: A hybrid architecture for accurate and lightweight restaurant recommendations. Cognitive Computation and Systems, 5(4), 265–279. https://doi.org/10.1049/ccs2.12090
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