Recurrent ALBERT for recommendation: A hybrid architecture for accurate and lightweight restaurant recommendations

Abstract

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.