Attentive Contextual Denoising Autoencoder for Recommendation

Abstract

Personalized recommendation has become increasingly pervasive nowadays. Users receive recommendations on products, movies, point-of-interests and other online services. Traditional collaborative filtering techniques have demonstrated effectiveness in a wide range of recommendation tasks, but they are unable to capture complex relationships between users and items. There is a surge of interest in applying deep learning to recommender systems due to its nonlinear modeling capacity and recent success in other domains such as computer vision and speech recognition. However, prior work does not incorporate contexual information, which is usually largely available in many recommendation tasks. In this paper, we propose a deep learning based model for contexual recommendation. Specifically, the model consists of a denoising autoencoder neural network architecture augmented with a context-driven attention mechanism, referred to as Attentive Contextual Denoising Autoencoder (ACDA). The attention mechanism is utilized to encode the contextual attributes into the hidden representation of the user's preference, which associates personalized context with each user's preference to provide recommendation targeted to that specific user. Experiments conducted on multiple real-world datasets from Meetup and Movielens on event and movie recommendations demonstrate the effectiveness of the proposed model over the state-of-the-art recommenders.