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HeteroSales: Utilizing Heterogeneous Social Networks to Identify the Next Enterprise Customer

Authors:

Philip S. YuAuthors Info & Claims

WWW '16: Proceedings of the 25th International Conference on World Wide Web

Pages 41 - 50

https://doi.org/10.1145/2872427.2883000

Published: 11 April 2016 Publication History

Abstract

Nowadays, a modern e-commerce company may have both online sales and offline sales departments. Normally, online sales attempt to sell in small quantities to individual customers through broadcasting a large amount of emails or promotion codes, which heavily rely on the designed backend algorithms. Offline sales, on the other hand, try to sell in much larger quantities to enterprise customers through contacts initiated by sales representatives, which are more costly compared to online sales. Unlike many previous research works focusing on machine learning algorithms to support online sales, this paper introduces an approach that utilizes heterogenous social networks to improve the effectiveness of offline sales. More specifically, we propose a two-phase framework, HeteroSales, which first constructs a company-to-company graph, a.k.a. Company Homophily Graph (CHG), from semantics based meta-path learning, and then adopts label propagation on the graph to predict promising companies that we may successfully close an offline deal with. Based on the statistical analysis on the world's largest professional social network, LinkedIn, we demonstrate interesting discoveries showing that not all the social connections in a heterogeneous social network are useful in this task. In other words, some proper data preprocessing is essential to ensure the effectiveness of offline sales. Finally, through the experiments on LinkedIn social network data and third-party offline sales records, we demonstrate the power of HereroSales to identify potential enterprise customers in offline sales.

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https://dl.acm.org/doi/10.1145/3637528.3671643
Liu ZZhang YAbedin MWang JYang HGao YChen Y(2024)Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser predictionJournal of Retailing and Consumer Services10.1016/j.jretconser.2024.10385479(103854)Online publication date: Jul-2024
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Published In

cover image ACM Other conferences

WWW '16: Proceedings of the 25th International Conference on World Wide Web

April 2016

1482 pages

ISBN:9781450341431

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Universit� du Qu�bec � Montr�al, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright � 2016 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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LinkedIn Corporation
National Science Foundation
Google Research Award

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WWW '16

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IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Qu�bec, Montr�al, Canada

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WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Liang YZhao JLi DFeng JZhang CDing XHao JHe RBaeza-Yates RBonchi F(2024)Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One AssignmentsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671643(5363-5374)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671643
Liu ZZhang YAbedin MWang JYang HGao YChen Y(2024)Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser predictionJournal of Retailing and Consumer Services10.1016/j.jretconser.2024.10385479(103854)Online publication date: Jul-2024
https://doi.org/10.1016/j.jretconser.2024.103854
Huang LWang CChao H(2021)HM-Modularity: A Harmonic Motif Modularity Approach for Multi-Layer Network Community DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.295653233:6(2520-2533)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TKDE.2019.2956532
Wang PChen HZhu YShen HZhang YPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Unified Collaborative Filtering over Graph EmbeddingsProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331224(155-164)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331224
Zhang JDong BYu P(2019)Deep Diffusive Neural Network based Fake News Detection from Heterogeneous Social Networks2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005556(1259-1266)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9005556
Zhang JYu P(2018)Broad Learning:ACM SIGKDD Explorations Newsletter10.1145/3229329.322933320:1(24-50)Online publication date: 29-May-2018
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Skopal TPeška LHolubová IPaščenko PHučín J(2018)Advanced Analytics of Large Connected Data Based on Similarity ModelingSimilarity Search and Applications10.1007/978-3-030-02224-2_16(209-216)Online publication date: 4-Oct-2018
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Zhang JCui LYu PLv YLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)BL-ECDProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133026(859-868)Online publication date: 6-Nov-2017
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Grad-Gyenge LKiss AFilzmoser PBielikova MHerder EDesmarais MCena F(2017)Graph Embedding Based Recommendation Techniques on the Knowledge GraphAdjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3099023.3099096(354-359)Online publication date: 9-Jul-2017
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Shi CYu PShi CYu P(2017)Survey of Current DevelopmentsHeterogeneous Information Network Analysis and Applications10.1007/978-3-319-56212-4_2(13-30)Online publication date: 26-May-2017
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