Item Recommendation for Word-of-Mouth Scenario in Social E-Commerce

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 (sharer), can share a link of a product to their social-connected friends (receiver). Once a receiver purchases the product, the sharer can earn commission provided by the platform. To promote the 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 paper, we proposed models of TriM (short for Triad based word-of-Mouth recommendation) and TriM-Joint to combine receivers' own interest and sharers' influence and capture the ternary relation. 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 7.4% ∼ 14.4% respectively.