Learning Graph-based Embedding For Time-Aware Product Recommendation

In this paper, we propose a novel Product Graph Embedding (PGE) model to investigate time-aware product recommendation by lever-aging the network representation learning technique. Our model captures the sequential influences of products by transforming the historical purchase records into a product graph. Then the product can be transformed into a low dimensional vector by the network embedding model. Once products are projected into the latent space, we present a novel method to compute user's latest preferences, which projects users into the same latent space as products. This method is based on time-decay functions and the embedding of sequential products that the user purchased. Thus, relatedness between a product and a user can be measured by the similarity between the embedding vectors which represent the product and the user's preferences. The experimental results on purchase records crawled from JINGD ONG, show the superiority of our proposed framework for personalized product recommendation.