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research-article

Recommending branded products from social media

Authors:

Yongzheng Zhang,

Marco PennacchiottiAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 77 - 84

https://doi.org/10.1145/2507157.2507170

Published: 12 October 2013 Publication History

Abstract

E-commerce companies are increasingly encouraging their users to connect to social media venues such as Facebook and Pinterest. The main strategic goal of such social connections is to boost user interaction and adoption on social media. However only a few efforts have been focused so far on leveraging users' social profiles to personalize the e-commerce experience and to recommend products of interest. In this paper, we start exploring this topic by investigating if a user's social media profile can be used to predict and recommend what type of products and what brands the social user is more likely to buy. More specifically, we study the correlation between the brands liked by the user on social media sites and those purchased on an e-commerce site. We then leverage these correlations in a brand prediction system, showing that social media can be effectively used to recommend branded products when user-user collaborative filtering techniques are used.

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Cited By

Mahmoud ABerman AGrigoriou NSolakis K(2023)Investigating gender as a moderator of extreme-context perception effects on behavioural tendencies towards fashion brands on Instagram in West AfricaJournal of Marketing Communications10.1080/13527266.2023.2278058(1-23)Online publication date: 9-Nov-2023
https://doi.org/10.1080/13527266.2023.2278058
Jalali SHosseini M(2021)Social collaborative filtering using local dynamic overlapping community detectionThe Journal of Supercomputing10.1007/s11227-021-03734-3Online publication date: 29-Mar-2021
https://doi.org/10.1007/s11227-021-03734-3
Chakraborty KBhattacharyya SBag R(2020)A Survey of Sentiment Analysis from Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29569577:2(450-464)Online publication date: Apr-2020
https://doi.org/10.1109/TCSS.2019.2956957
Show More Cited By

Index Terms

Recommending branded products from social media
1. Applied computing
  1. Electronic commerce
2. Information systems
  1. Information storage systems
    1. Storage architectures
      1. Storage network architectures

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Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2013

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RecSys '13

Sponsor:

SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Citations

Cited By

Mahmoud ABerman AGrigoriou NSolakis K(2023)Investigating gender as a moderator of extreme-context perception effects on behavioural tendencies towards fashion brands on Instagram in West AfricaJournal of Marketing Communications10.1080/13527266.2023.2278058(1-23)Online publication date: 9-Nov-2023
https://doi.org/10.1080/13527266.2023.2278058
Jalali SHosseini M(2021)Social collaborative filtering using local dynamic overlapping community detectionThe Journal of Supercomputing10.1007/s11227-021-03734-3Online publication date: 29-Mar-2021
https://doi.org/10.1007/s11227-021-03734-3
Chakraborty KBhattacharyya SBag R(2020)A Survey of Sentiment Analysis from Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29569577:2(450-464)Online publication date: Apr-2020
https://doi.org/10.1109/TCSS.2019.2956957
Vajjhala NRakshit SOshogbunu MSalisu S(2020)Novel User Preference Recommender System Based on Twitter Profile AnalysisSoft Computing Techniques and Applications10.1007/978-981-15-7394-1_7(85-93)Online publication date: 28-Nov-2020
https://doi.org/10.1007/978-981-15-7394-1_7
Lee HPark HLee W(2018)Implementation of Customer Behavior Evaluation System Using Real-time Web Log Stream DataThe Journal of Korean Institute of Information Technology10.14801/jkiit.2018.16.12.116:12(1-11)Online publication date: 31-Dec-2018
https://doi.org/10.14801/jkiit.2018.16.12.1
Luu MLim E(2018)Do your friends make you buy this brand?Data Mining and Knowledge Discovery10.1007/s10618-017-0535-932:2(287-319)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10618-017-0535-9
Contratres FAlves-Souza SFilgueiras LDeSouza L(2018)Sentiment Analysis of Social Network Data for Cold-Start Relief in Recommender SystemsTrends and Advances in Information Systems and Technologies10.1007/978-3-319-77712-2_12(122-132)Online publication date: 17-May-2018
https://doi.org/10.1007/978-3-319-77712-2_12
Dong YJiang W(2018)Brand purchase prediction based on time‐evolving user behaviors in e‐commerceConcurrency and Computation: Practice and Experience10.1002/cpe.488231:1Online publication date: 23-Oct-2018
https://doi.org/10.1002/cpe.4882
LI YJIANG JLIU T(2017)Inferring User Consumption Preferences from Social MediaIEICE Transactions on Information and Systems10.1587/transinf.2016EDP7265E100.D:3(537-545)Online publication date: 2017
https://doi.org/10.1587/transinf.2016EDP7265
Bansal AKumar SYadav RDhage N(2017)A review on cross domain recommendation2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)10.1109/ICECA.2017.8212739(617-620)Online publication date: Apr-2017
https://doi.org/10.1109/ICECA.2017.8212739
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