Abstract
On E-commerce platforms, understanding the relationships (e.g., substitute and complement) among
products from user’s explicit feedback, such as
users’ online transactions, is of great importance
to boost extra sales. However, the significance
of such relationships is usually neglected by existing recommender systems. In this paper, we
propose a semi-supervised deep embedding model,
namely, Substitute Products Embedding Model
(SPEM), which models the substitutable relationship between products by preserving the secondorder proximity, negative first-order proximity and
semantic similarity in a product co-purchasing
graph based on user’s purchasing behaviours. With
SPEM, the learned representations of two substitutable products align closely in the latent embedding space. Extensive experiments on seven realworld datasets are conducted, and the results verify
that our model outperforms state-of-the-art baselines