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DHRD - Delivery Hero Recommendation Dataset: A Novel Dataset for Benchmarking Recommendation Algorithms

Delivery Hero

by Yernat Assylbekov, Raghav Bali, Luke Bovard, Christian Klaue

[Paper] [Blog]

Code repository for the Delivery Hero Recommendation Dataset (DHRD), which provides a diverse real-world dataset for researchers. DHRD comprises over a million food delivery orders from three distinct cities, encompassing thousands of vendors and an extensive range of dishes, serving a combined customer base of over a million individuals. We discuss the challenges associated with such real-world datasets. By releasing DHRD, researchers are empowered with a valuable resource for building and evaluating recommender systems, paving the way for advancements in this domain.

Getting the data

The training and testing data is publicly hosted and can be downloaded.

License

The DHRD dataset is released under the MIT license. See LICENSE for additional details.

Citing DHRD

If you find this dataset useful, please consider giving a star ⭐ and citation 🦖:

@inproceedings{10.1145/3604915.3610242,
author = {Assylbekov, Yernat and Bali, Raghav and Bovard, Luke and Klaue, Christian},
title = {Delivery Hero Recommendation Dataset: A Novel Dataset for Benchmarking Recommendation Algorithms},
year = {2023},
isbn = {9798400702419},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3604915.3610242},
doi = {10.1145/3604915.3610242},
abstract = {In this paper we propose Delivery Hero Recommendation Dataset (DHRD), a novel real-world dataset for researchers. DHRD comprises over a million food delivery orders from three distinct cities, encompassing thousands of vendors and an extensive range of dishes, serving a combined customer base of over a million individuals. We discuss the challenges associated with such real-world datasets. By releasing DHRD, researchers are empowered with a valuable resource for building and evaluating recommender systems, paving the way for advancements in this domain.},
booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems},
pages = {1042–1044},
numpages = {3},
location = {Singapore, Singapore},
series = {RecSys '23}
}

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