Name
Abstract | ||
|---|---|---|
For efficient and economical operation, restaurant owners need to accurately estimate the number of future customers. In this paper, we propose an approach to predict how many future visi-tors will go to a restaurant using big data and supervised learning. The included big data involves restaurant information, historical visits and historical reservations. With features constructed from the big data, our approach generates predictions by performing regression using a mix of K-Nearest-Neighbour, Random Forests and XGBoost. We evaluate our approach using large-scale realworld datasets from two restaurant booking websites. The eval- uation results show the effectiveness of our approach, as well as useful insights for future work. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLC.2018.8526963 | 2018 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
Machine learning,Big data,Business intelligence,Random forests,XGBoost | Training set,Task analysis,Computer science,Support vector machine,Supervised learning,Artificial intelligence,Random forest,Big data,Machine learning,Goto | Conference |
Volume | ISSN | ISBN |
1 | 2160-133X | 978-1-5386-5215-2 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Ma | 1 | 21 | 4.12 |
| Yanshan Tian | 2 | 0 | 0.34 |
| Chu Luo | 3 | 84 | 12.18 |
| Yuehui Zhang | 4 | 3 | 2.73 |