Modeling User Interests with Online Social Network Influence by Memory Augmented Sequence Learning

Online social networks, such as Facebook and Twitter, enable users to share their shopping/travel experiences with their friends. However the influence on users' decision-making on next visit/buy has sparse research exposure, by accurately modeling long-term user behaviors from historical data. The existing methods do not fully take advantage of the underlying social networks to model user interests, nor they have not modeled long-term transitional behavior patterns. In this paper, we propose a novel Social Influence aware and Memory augmented Sequence learning (SIMS) model, on what a user will likely buy/visit next. Specifically, SIMS first learns a representation for the visiting/purchasing sequence of each user using the sequence-to-sequence learning method. Then it predicts the interest of a user by integrating the representation of his/her own sequence, with another representation of the corresponding social influence, which is learned using an autoencoder-based model. In addition, we leverage an emerging memory augmented neural network, Differentiable Neural Computer (DNC), to further improve prediction accuracy. We conduct extensive experiments to evaluate the proposed model using three real-world datasets, Yelp, Epinions and Ciao. When compared with 10 other baselines and state-of-the-art solutions, the experimental results show that 1) the proposed model significantly outperforms all other methods in terms of various accuracy-related metrics; 2) the proposed social influence modeling and memory augmentation do lead to the performance gain.