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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
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) | ||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Z Bibliography. Library Science. Information Resources > ZA Information resources |
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| 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: |
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| 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: |
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| 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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