Search

Matrix factorization is widely used in collaborative filtering, especially when the data are extremely large and sparse. To deal with the scale and sparsity problem of data, several recommender models adopt users and items' side information to improve the recommendation results. However, some existing works do not perform well enough for they are not effectively use the side information. For example, using bag-of-words model, topic model to gain the latent representation of words or merely utilizing items or users' side information, leads to the result that the performance deteriorates, especially when rating dataset is extremely large and sparse. To overcome the data sparsity problem, we present a hybrid model named robust attention convolutional matrix factorization (RACMF) model, which is composed of attention convolutional neural network (ACNN) and additional stacked denoising autoencoder (aSDAE); ACNN and aSDAE are used to extract the items' and users' latent factors, respectively. The experimental results show that our RACMF model has good prediction ability, even when the rating data are sparse or the scale of rating data is large. What's more, compared with the state-of-the-art model PHD, the present model RACMF increased the accuracy rate on ML-100k, ML-1m, ML-10m and AIV-6 datasets by 4.80%, 0.57%, 1.98% and 3.67%, respectively. [ABSTRACT FROM AUTHOR]

Most of the existing modeling methods of combining metadata are based on the same user I item attribute weights, so that the key relationships between users and items are not significant, and it is difficult to obtain better recommendation performance. To solve the above problems, this paper proposed a method of deep joint learning recommendation based on metadata and attention mechanism. It used double deep network joint learning, one of the networks implemented matrix nonlinear decomposition based on implicit feedback data to learn user/ project personalization relationship, and another network automatically captured influence the user/ item key attributes to recommend by using attention mechanism, through the user preference relation with weighted of different attributes modeling highlighted the extended model. Experimental results show that the proposed recommendation algorithm has better recommendation performance on two public datasets of MovieLens 100K and MovieLens lM. [ABSTRACT FROM AUTHOR]