Christoph Trattner
Denis Parra
Lukas Eberhard
ABSTRACT
In this paper we present the latest results of a recently started project that aims at studying the extent to which links between buyers and sellers, i.e. trading interactions in online trading platforms, can be predicted from external knowledge sources such as online social networks. To that end, we conducted a large-scale experiment on data obtained from the virtual world Second Life. As our results reveal, online social network data bears a significant potential (28% over the baseline) to predict links between buyers and sellers in online trading platforms.
- S. Guo, M. Wang, and J. Leskovec. The role of social networks in online shopping: information passing, price of trust, and consumer choice. In ACM Conference on Electronic Commerce, pages 157--166, 2011. Google Scholar
Digital Library
- M. Steurer and C. Trattner. Who will interact with whom? a case-study in second life using online social network and location-based social network features to predict interactions between users. In Ubiquitous Social Media Analysis, volume 8329 of Lecture Notes in Computer Science, pages 108--127. Springer Berlin Heidelberg, 2013.Google ScholarCross Ref
- Y. Zhang and M. Pennacchiotti. Predicting purchase behaviors from social media. In Proceedings of the 22Nd International Conference on World Wide Web, WWW '13, pages 1521--1532, 2013. Google ScholarDigital Library
Index Terms
- Who will trade with whom?: predicting buyer-seller interactions in online trading platforms through social networks
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