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We know what you want to buy : a demographic-based system for product recommendation on microblogs

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Zhao, Xin Wayne, Guo, Yanwei, He, Yulan, Jiang, Han, Wu, Yuexin and Li, Xiaoming (2014) We know what you want to buy : a demographic-based system for product recommendation on microblogs. In: 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, USA, 24-27 Aug 2014. Published in: KDD '14 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining pp. 1935-1944. ISBN 9781450329569. doi:10.1145/2623330.2623351

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Official URL: http://dx.doi.org/10.1145/2623330.2623351

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Abstract

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Recommender systems (Information filtering), Electronic commerce -- Demographic aspects, Microblogs
Series Name: KDD: Knowledge Discovery and Data Mining
Journal or Publication Title: KDD '14 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher: ACM
ISBN: 9781450329569
Book Title: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14
Official Date: August 2014
Dates:
DateEvent
August 2014Published
10 May 2014Accepted
Page Range: pp. 1935-1944
DOI: 10.1145/2623330.2623351
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 6 February 2019
Date of first compliant Open Access: 7 February 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
2014CB340403 [MSTPRC] Ministry of Science and Technology of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002855
2014CB340405[MSTPRC] Ministry of Science and Technology of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002855
61272340[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
EP/L010690/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
UNSPECIFIEDMicrosoft Researchhttp://dx.doi.org/10.13039/100006112
Conference Paper Type: Paper
Title of Event: 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Type of Event: Conference
Location of Event: New York, USA
Date(s) of Event: 24-27 Aug 2014

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