demonstration

GPR: Global Personalized Restaurant Recommender System Leveraging Billions of Financial Transactions

Authors:

Mangesh Bendre,

Mahashweta Das,

Fei Wang,

Hao YangAuthors Info & Claims

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Pages 914 - 917

https://doi.org/10.1145/3437963.3441709

Published: 08 March 2021 Publication History

Get Access

Abstract

In this paper, we demonstrate our Global Personalized Recommender (GPR) system for restaurants. GPR does not use any explicit reviews, ratings, or domain-specific metadata but rather leverages over 3 billion anonymized payment transactions to learn user and restaurant behavior patterns. The design and development of GPR have been challenging, primarily due to the scale and skew of the data. Our system supports over 450M cardholders from over 200 countries and 2.5M restaurants in over 35K cities worldwide, respectively. Additionally, GPR being a global recommender system, needs to account for the regional variations in people's food choices and habits. We address the challenges by combining three different recommendation algorithms instead of using a single revolutionary model in the backend. The individual recommendation models are scalable and adapt to varying data skew challenges to ensure high-quality personalized recommendations for any user anywhere in the world.

References

[1]

Xavier Amatriain and Bamshad Mobasher. 2014. The recommender problem revisited: morning tutorial. In The 20th ACM SIGKDD'14, New York, NY, USA - August 24 - 27, 2014. ACM, 1971.

Digital Library

Google Scholar

[2]

Min Du, Robert Christensen, Wei Zhang, and Feifei Li. 2019. Pcard: Personalized restaurants recommendation from card payment transaction records. In The World Wide Web Conference. 2687--2693.

Digital Library

Google Scholar

[3]

Amy Fleming. 2013. The geography of taste: how our food preferences are formed. The Guardian UK (2013).

Google Scholar

[4]

Adit Krishnan, Mahashweta Das, Mangesh Bendre, and Hao Yang. 2020. Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation. In The 43rd ACM SIGIR International Conference on Research and Development in Information Retrieval, SIGIR '20, Xi'an, China - July 25 - 30, 2020 . ACM .

Digital Library

Google Scholar

[5]

Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, Vol. 7, 1 (2003), 76--80.

Google Scholar

[6]

Yves Raimond and Justin Basilico. 2016. Recommending for the World. The Netflix Tech Blog (2016).

Google Scholar

Cited By

View all

Bendre MDas MRakesh VWang FYang H(2022)bigg2vec: Fast and Memory-Efficient Representation Learning for Billion-Scale Graphs on a Single Machine2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020666(835-843)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020666
Wang YZheng YPeng YYeh MZhuang ZMahashweta DMangesh BLi FZhang WPhillips J(2021)Constrained Non-Affine Alignment of Embeddings2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00179(1403-1408)Online publication date: Dec-2021
https://doi.org/10.1109/ICDM51629.2021.00179

Index Terms

GPR: Global Personalized Restaurant Recommender System Leveraging Billions of Financial Transactions
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Exploiting Dining Preference for Restaurant Recommendation
WWW '16: Proceedings of the 25th International Conference on World Wide Web

The wide adoption of location-based services provide the potential to understand people's mobility pattern at an unprecedented level, which can also enable food-service industry to accurately predict consumers' dining behavior. In this paper, based on ...
Multi-source Information Fusion for Personalized Restaurant Recommendation
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

In this paper, we study the problem of personalized restaurant recommendations. Specifically, we develop a probabilistic factor analysis framework, named RMSQ-MF, which has the ability in exploiting multi-source information, such as the users' task, ...
A restaurant recommendation approach with the contextual information

Recommender systems can recommend products by analyzing the interests and habits of users. To make more efficient recommendation, the contextual information should be collected in recommendation algorithms. In the restaurant recommendation, the location ...

Comments

Information & Contributors

Information

Published In

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

March 2021

1192 pages

ISBN:9781450382977

DOI:10.1145/3437963

General Chairs:
Liane Lewin-Eytan
Amazon, Israel
,
David Carmel
Amazon, Israel
,
Elad Yom-Tov
Microsoft, Israel
,
Program Chairs:
Eugene Agichtein
Emory University and Amazon, USA
,
Evgeniy Gabrilovich
Google Health, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2021

Check for updates

Author Tags

Qualifiers

Demonstration

Conference

WSDM '21

Sponsor:

WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining

March 8 - 12, 2021

Virtual Event, Israel

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
216
Total Downloads

Downloads (Last 12 months)30
Downloads (Last 6 weeks)7

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Bendre MDas MRakesh VWang FYang H(2022)bigg2vec: Fast and Memory-Efficient Representation Learning for Billion-Scale Graphs on a Single Machine2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020666(835-843)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020666
Wang YZheng YPeng YYeh MZhuang ZMahashweta DMangesh BLi FZhang WPhillips J(2021)Constrained Non-Affine Alignment of Embeddings2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00179(1403-1408)Online publication date: Dec-2021
https://doi.org/10.1109/ICDM51629.2021.00179

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Exploiting Dining Preference for Restaurant Recommendation

Multi-source Information Fusion for Personalized Restaurant Recommendation

A restaurant recommendation approach with the contextual information