Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

Abstract

The era of the Internet of things (IoT) has marked a continued exploration of applications and services that canmake people's lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting themost appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques including collaborative filtering (CF), which is most widely adopted when building recommendation systems suffer from rating sparsity and cold-start problems, preventing them from providing high quality recommendations. Inspired by the great success of deep learning in awide range of fields, thiswork introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations. The proposed deep learning model is designed as a hybrid architecture with three key networks, namely autoencoder (AE),multilayered perceptron (MLP), and generalized matrix factorization (GMF). The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel. Next, MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features. Finally, the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF andMLP networks.We conducted extensive experiments on two benchmark datasets, MoiveLens100K and MovieLens1M, using four standard evaluation metrics. Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance. Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CFmethods with resistance to rating sparsity and cold-start problems.