In recommender systems (RS), many models are designed to predict ratings of items for the target user. To improve the performance for rating prediction, some studies have introduced tags into recommender systems. Tags benefit RS considerably, however, they are also redundant and ambiguous. In this paper, we propose a hybrid deep learning model TRSDL (tag-aware recommender system based on deep learning) to improve the performance of tag-aware recommender systems (TRS). First, TRSDL uses pre-trained word embeddings to represent user-defined tags, and constructs item and user profiles based on the items’ tags set and users’ tagging behaviors. Then, it utilizes deep neural networks (DNNs) and recurrent neural networks (RNNs) to extract the latent features of items and users, respectively. Finally, it predicts ratings from these latent features. The model not only addresses tag limitations and takes advantage of semantic tag information but also learns more advanced implicit features via deep structures. We evaluated our proposed approach and several baselines on MovieLens-20 m, and the experimental results demonstrate that TRSDL significantly outperforms all the baselines (including the state-of-the-art models BiasedMF and I-AutoRec). In addition, we also explore the impacts of network depth and type on model performance.
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