research-article

CORALS: Who Are My Potential New Customers? Tapping into the Wisdom of Customers' Decisions

Authors:

Chelsea J.-T. Ju,

Wei WangAuthors Info & Claims

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Pages 69 - 77

https://doi.org/10.1145/3289600.3290995

Published: 30 January 2019 Publication History

Abstract

Identifying and recommending potential new customers for local businesses are crucial to the survival and success of local businesses. A key component to identifying the right customers is to understand the decision-making process of choosing a business over the others. However, modeling this process is an extremely challenging task as a decision is influenced by multiple factors. These factors include but are not limited to an individual's taste or preference, the location accessibility of a business, and the reputation of a business from social media. Most of the recommender systems lack the power to integrate multiple factors together and are hardly extensible to accommodate new incoming factors. In this paper, we introduce a unified framework, CORALS, which considers the personal preferences of different customers, the geographical influence, and the reputation of local businesses in the customer recommendation task. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 state-of-the-art methods using two real-world datasets. The results demonstrate that CORALS outperforms all these baselines by a significant margin in most scenarios. In addition to identifying potential new customers, we also break down the analysis for different types of businesses to evaluate the impact of various factors that may affect customers' decisions. This information, in return, provides a great resource for local businesses to adjust their advertising strategies and business services to attract more prospective customers.

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Cited By

Banerjee SSen AZahay D(2023)The effect of in-store electronic word of mouth on local competitor spillovers in the quick service restaurant industryElectronic Commerce Research10.1007/s10660-023-09742-0Online publication date: 28-Jul-2023
https://doi.org/10.1007/s10660-023-09742-0
Li YLin YGao YKhan L(2022)Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling for Social Science2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892911(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892911
Li ZCheng WXiao HYu WChen HWang WDemartini GZuccon GCulpepper JHuang ZTong H(2021)You Are What and Where You AreProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481962(3945-3954)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481962
Show More Cited By

Index Terms

CORALS: Who Are My Potential New Customers? Tapping into the Wisdom of Customers' Decisions
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems
      2. Location based services

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cover image ACM Conferences

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

January 2019

874 pages

ISBN:9781450359405

DOI:10.1145/3289600

General Chairs:
J. Shane Culpepper
RMIT University
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Paul N. Bennett
Microsoft
,
Kristina Lerman
University of Southern California

Copyright © 2019 ACM.

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Published: 30 January 2019

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February 11 - 15, 2019

Melbourne VIC, Australia

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WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;

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

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Cited By

Banerjee SSen AZahay D(2023)The effect of in-store electronic word of mouth on local competitor spillovers in the quick service restaurant industryElectronic Commerce Research10.1007/s10660-023-09742-0Online publication date: 28-Jul-2023
https://doi.org/10.1007/s10660-023-09742-0
Li YLin YGao YKhan L(2022)Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling for Social Science2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892911(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892911
Li ZCheng WXiao HYu WChen HWang WDemartini GZuccon GCulpepper JHuang ZTong H(2021)You Are What and Where You AreProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481962(3945-3954)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481962
Wu XHuang CZhang CChawla N(2020)Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event ForecastingProceedings of The Web Conference 202010.1145/3366423.3380296(2320-2330)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380296
Li RWu XWu XWang W(2020)Few-Shot Learning for New User Recommendation in Location-based Social NetworksProceedings of The Web Conference 202010.1145/3366423.3379994(2472-2478)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3379994
Huang CZhang CZhao JWu XYin DChawla N(2019)MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event ForecastingThe World Wide Web Conference10.1145/3308558.3313730(717-728)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313730
Antelmi ACordasco GKamiński BPrałat PScarano VSpagnuolo CSzufel P(2019)SimpleHypergraphs.jl—Novel Software Framework for Modelling and Analysis of HypergraphsAlgorithms and Models for the Web Graph10.1007/978-3-030-25070-6_9(115-129)Online publication date: 4-Jul-2019
https://doi.org/10.1007/978-3-030-25070-6_9

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