Integrating metadata into deep autoencoder for handling prediction task of collaborative recommender system

Nowadays, Deep learning (DL) techniques have been proven successful as learning techniques in various research fields ranging from computer vision to social networks. The approach of DL is flourishing in the field of recommender systems (RS). Researchers have deployed metadata or auxiliary information using DL approaches in diverse applications in the last decade to achieve better recommendation accuracy. Thus, the metadata plays a vital role in obtaining a better user-item interaction. At the same time, existing techniques are based on fixed user and item factors. Therefore, the model does not correctly identify actual latent factors representation, resulting in a high prediction error. To handle this problem, a user metadata embedding using a deep autoencoder RS model called "Metadata Embedding Deep AutoEncoder (MEDAE)" based collaborative filtering is proposed. MEDAE model takes embeds user metadata such as demographics along with the rating data. The MEDAE model consists of an embedding layer, Encoder, and Decoder. The embedding layer generates embedding or latent features of the users, items, and metadata; Encoder receives concatenated features of the user, item, and metadata, then encodes the inputs and passes them to the decoder; and the decoder reconstructs the output. To test the effectiveness of proposed model Root Mean Squared Error and Mean Absolute Error measures are used. Different architectures (like Big-Small-Big (BSB) (5), BSB (3), Small-Big-Small (3), and SBS (5)) of the MEDAE model are evaluated on MovieLens datasets along with different parameters such as activation functions (ELU and SELU) and regularization and results concluded that the MEDAE with SBS (3) and ELU + SELU component improves 4% of RMSE and 2% MAE over the baseline methods.