A Neural Labeled Network Embedding Approach to Product Adopter Prediction.

On e-commerce websites, it is common to see that a user purchases products for others. The person who actually uses the product is called the adopter. Product adopter information is important for learning user interests and understanding purchase behaviors. However, effective acquisition or prediction of product adopter information has not been well studied. Existing methods mainly rely on explicit extraction patterns, and can only identify exact occurrences of adopter mentions from review data. In this paper, we propose a novel Neural Labeled Network Embedding approach (NLNE) to inferring product adopter information from purchase records. Compared with previous studies, our method does not require any review text data, but try to learn effective prediction model using only purchase records, which are easier to obtain than review data. Specially, we first propose an Adopter-labeled User-Product Network Embedding (APUNE) method to learn effective representations for users, products and adopter labels. Then, we further propose a neural prediction approach for inferring product adopter information based on the learned embeddings using APUNE. Our NLNE approach not only retains the expressive capacity of labeled network embedding, but also is endowed with the predictive capacity of neural networks. Extensive experiments on two real-world datasets (i.e., JingDong and Amazon) demonstrate the effectiveness of our model for the studied task.