Hybrid deep neural networks for recommender systems
Abstract More recently, deep neural networks received much attention from the recommender systems community where CNNs and RNNs were applied in different recommendation contexts and achieved state of the art results on many publicly available benchmarks. While the success of the neural models came from their high expressiveness, the uninterpretability problem still one of their main drawbacks, which can have a negative side effect on the whole learning process. A good candidate to reduce the uninterpretability is using Probabilistic Soft Logic (PSL) which showed a strong performance in dealing with this problem. Moreover, sparsity is another major problem of the most previous recommendation systems. In this paper, we introduce a new recommender system framework based on the generalized distillation principle that combines two modeling approaches: PSL for the knowledge driven modeling approach and deep neural networks for the data driven approach. Experimental results on publicly available datasets show that our method significantly outperforms the previous state of the art based on deep neural networks emphasizing the utility of PSL rules in handling inconsistencies, reducing the uninterpretability of the neural models and shows promising results in dealing with sparsity.
Neurocomputing