Joint Deep Network With Auxiliary Semantic Learning for Popular Recommendation

There is a cold-start problem in the recommendation system field, which is how to profile new users and new items. The popular recommendation algorithm is an important solution to the cold-start problem. In this paper, we propose a new joint deep network model with auxiliary semantic learning for the popular recommendation algorithm (DMPRA). First, we define the items with a large quantity of review data and high ratings as the popular recommended items. Second, we introduce text analysis into the popular recommendation algorithm. We use the optimized CharCNN networks to learn the auxiliary semantic vectors from the users' reviews. Then, we use the Factorization Machine (FM) component and deep component to learn the corresponding vector representations of the items' attribute features. We use convolution to simulate the interaction of hidden latent vectors. This method can make the vectors interact more satisfactorily than traditional interactive representation methods. Finally, we provide the users with a reasonable popular recommendation list. The experimental results show that our algorithm can improve the AUC (area under the ROC curve) and Logloss (cross-entropy) of the popular items' prediction. In addition, we provide relevant explanations for some useful phenomena.