Hybrid Deep Embedding for Recommendations with Dynamic AspectLevel Explanations. Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different itemsusers have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspectlevel explanations are more persuasive than itemlevel or userlevel ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspectbased explainable recommendations, which can make recommendations with dynamic aspectlevel explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preferencequality vectors for the generation of aspectlevel explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive useritem interactions. Particularly, as the aspect preferencequality of usersitems is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at urlthis https URL

A Novel Hybrid Deep Recommendation System to Differentiate Userxe2x80x99s Preference and Itemxe2x80x99s Attractiveness Abstract   With the fast development of online Ecommerce Websites and mobile applications, usersxe2x80x99 auxiliary information as well as productsxe2x80x99 textual information can be easily collected to form a vast amount of training data. Therefore, research efforts are urgently needed to make customized recommendations using such large but sparse data. Deep recommendation model is a natural choice for this research issue. However, most existing approaches try to investigate either userxe2x80x99s auxiliary information such as age and zipcode, or itemxe2x80x99s textual information such as product descriptions, reviews or comments. Therefore, it is desired to see whether userxe2x80x99s auxiliary information and itemxe2x80x99s textual information could be modeled simultaneously. This paper proposes a novel approach which is essentially a hybrid probabilistic matrix factorization model. Particularly, it has two sub components. One component tries to predict userxe2x80x99s rating scores by capturing userxe2x80x99s personal preferences extracted from auxiliary information. Another component tries to model itemxe2x80x99s textual attractiveness to different users via a proposed attention based convolutional neural network. We then propose a global objective function and optimize these two sub components under a unified framework. Extensive experiments are performed on five realworld datasets, i.e., ML100K, ML1M, ML10M, AIV and Amazon sub dataset. The promising experimental results have demonstrated the superiority of our proposed approach when compared with both baseline models and stateoftheart deep recommendation approaches, i.e., PMF, CDL, CTR, ConvMF, ConvMF and DAttn with respect to RMSE criterion.