Item Recommendation for Word-of-Mouth Scenario in Social E-Commerce
Chen Gao, Chao Huang, Donghan Yu, Haohao Fu, Tzu-Heng Lin, Depeng Jin, Yong Li
Abstract
Social commerce, which is different from traditional e-commerce where people purchase products via initiative searching or recommendations from the platform, transforms a social community into an inclusive place to do business by enabling people to share products with their friends. A user ( <i>sharer</i> ), can share a link of a product to their social-connected friends ( <i>receiver</i> ). Once a receiver purchases the product, the sharer can earn commission provided by the platform. To promote sales, the platform can also assist sharers by providing product candidates which are more likely to be purchased during the social sharing. We define this task of generating sharing suggestions as item recommendation for word-of-mouth scenario, and to the best of our knowledge, this is a new task that has never been explored. In this article, we propose a <i>TriM</i> (short for <b>Tri</b> ad based word-of- <b>M</b> outh recommendation) model that can capture both the sharer’s influence and the receiver’s interest at the same time, which are two significant factors that determine whether the receiver will buy the product or not. Furthermore, with joint learning on two parts of interaction data to address data sparsity issue, our proposed TriM-Joint further improves the recommendation performance. By conducting experiments, we show that our proposed models achieve the best results compared to state-of-the-art models with significant improvements by at least <inline-formula><tex-math notation="LaTeX">$7.4\% \sim 14.4\%$</tex-math></inline-formula> respectively.