User Reactions Prediction Using Embedding Features

By the massive available people data in social media, many digital service providers exploit widely this information to improve their services by predicting future requirements of their customers. This prediction mainly needs to study users' previous behavior and interactions and identify their preferences to provide rigorous recommendations that fulfill their requirements more favorably. Meanwhile, experiments show the prediction methods which exploit representation learning instead of traditional hand-crafted features accomplish better results and more precise predictions. In this study, we take advantage of representation learning method to predict user's future interactions by extracting users embeddings from their reactions history and exploit them in predicting future reactions. In this approach, users embeddings are used in a neural network designed with one-hidden layer and a softmax function in the end layer in order to predict users reactions. The proposed method is evaluated when user embeddings come from two different sources; users reactions history and random walks on the user network. The performance of the method has been evaluated by using a large Flickr dataset including more than 2M users and 11M users reactions sequences. The results show outperforming of the prediction method when it uses the history of user reactions to derive user embeddings.