CROSS: Cross-platform Recommendation for Social E-Commerce

Tzu-Heng Lin, Chen Gao, Yong Li

FIB Lab, Tsinghua University

In SIGIR 2019

Abstract

Social e-commerce, as a new concept of e-commerce, uses social media as a new prevalent platform for online shopping. Users are now able to view, add to cart, and buy products within a single social media app. In this paper, we address the problem of cross-platform recommendation in social media, i.e., recommending products to users when they are shopping through social media. To the best of our knowledge, this is a new and important problem for all e-commerce companies (e.g. Amazon, Alibaba), but has never been studied before.

Existing cross-platform and social related recommendation methods cannot be applied directly for this problem since they do not co-consider the social information and the cross-platform characteristics together. To study this problem, we first investigate the heterogeneous shopping behaviors between traditional e-commerce app and social media. Based on these observations from data, we propose CROSS (Cross-platform Recommendation for Online Shopping in Social Media), a recommendation model utilizing not only user-item interaction data on both platforms, but also social relation data on social media. Extensive experiments on real-world online shopping dataset demonstrate that our proposed CROSS significantly outperforms existing state-of-the-art methods.

Paper

Link: arXiv, DOI (comming soon ...)

Citation: Tzu-Heng Lin, Chen Gao, and Yong Li. CROSS: Cross-platform Recommendation for Social E-Commerce. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19), July 21–25, 2019, Paris, France. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3331184.3331191

Dataset

Comming soon ...

Acknowledgement

This work was supported in part by The National Key Research and Development Program of China under grant 2017YFE0112300, the National Nature Science Foundation of China under 61861136003, 61621091 and 61673237, Beijing National Research Center for Information Science and Technology under 20031887521, and research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology.