Towards Explainable Personalized Recommendations by Learning from Usersxe2x80x99 Photos Abstract   Explaining the output of a complex system, such as a Recommender System (RS), is becoming of utmost importance for both users and companies. In this paper we explore the idea that personalized explanations can be learned as recommendation themselves. There are plenty of online services where users can upload some photos, in addition to rating items. We assume that users take these photos to reinforce or justify their opinions about the items. For this reason we try to predict what photo a user would take of an item, because that image is the argument that can best convince her of the qualities of the item. In this sense, an RS can explain its results and, therefore, increase its reliability. Furthermore, once we have a model to predict attractive images for users, we can estimate their distribution. Thus, the companies acquire a vivid knowledge about the aspects that the clients highlight of their products. The paper includes a formal framework that estimates the authorship probability for a given pair (user, photo). To illustrate the proposal, we use data gathered from TripAdvisor containing the reviews (with photos) of restaurants in six cities of different sizes.

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