Zhao, Wayne Xin, Li, Sui, He, Yulan, Chang, Edward Y., Wen, Ji-Rong and Li, Xiaoming (2016) Connecting social media to e-commerce : cold-start product recommendation using microblogging information. IEEE Transactions on Knowledge and Data Engineering, 28 (5). pp. 1147-1159. doi:10.1109/TKDE.2015.2508816 ISSN 1041-4347.

Preview

PDF WRAP-connecting-social-media-e-commerce-He-2018.pdf - Accepted Version - Requires a PDF viewer. Download (1214Kb) | Preview

Request Changes to record.

Abstract

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce Web sites support the mechanism of social login where users can sign on the Web sites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product Web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce Web sites to users at social networking sites in “cold-start” situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce Web sites (users who have social networking accounts and have made purchases on e-commerce Web sites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce Web sites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Item Type:	Journal Article
Subjects:	H Social Sciences > HF Commerce H Social Sciences > HM Sociology Q Science > QA Mathematics > QA75 (Please use QA76 Electronic Computers. Computer Science)
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Electronic commerce, Online social networks, Facebook (Firm), Twitter (Firm), Algorithms
Journal or Publication Title:	IEEE Transactions on Knowledge and Data Engineering
Publisher:	IEEE Computer Society
ISSN:	1041-4347
Official Date:	1 May 2016
Dates:	Date Event 1 May 2016 Published 7 December 2015 Accepted
Volume:	28
Number:	5
Page Range:	pp. 1147-1159
DOI:	10.1109/TKDE.2015.2508816
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	29 September 2018
Date of first compliant Open Access:	2 October 2018

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads