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Image based social networks are among the most popular social networking services in recent years. With a tremendous amount of images uploaded everyday, understanding users’ preferences on user-generated images and making recommendations have become an urgent need. In fact, many hybrid models have been proposed to fuse various kinds of side information (e.g., image visual representation, social network) and user-item historical behavior for enhancing recommendation performance. However, due to the unique characteristics of the user generated images in social image platforms, the previous studies failed to capture the complex aspects that influence users’ preferences in a unified framework. Moreover, most of these hybrid models relied on predefined weights in combining different kinds of information, which usually resulted in sub-optimal recommendation performance. To this end, in this paper, we develop a hierarchical attention model for social contextual image recommendation. In addition to basic latent user interest modeling in the popular matrix factorization based recommendation, we identify three key aspects (i.e., upload history, social influence, and owner admiration) that affect each user's latent preferences, where each aspect summarizes a contextual factor from the complex relationships between users and images. After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users’ latent interests with the identified key aspects. Specifically, by taking embeddings from state-of-the-art deep learning models that are tailored for each kind of data, the hierarchical attention network could learn to attend differently to more or less content. Finally, extensive experimental results on real-world datasets clearly show the superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Event recommendation is essential to help people find attractive events to attend, but it intrinsically faces cold-start problem. The previous studies exploit multiple contextual factors to overcome the cold-start problem in event recommendation. However, they do not consider the correlation among different contextual factors. Moreover, suggesting events for a group of users also has not been well studied. In this paper, we first discover the correlation between organizer and textual content, i.e., the events held by the same organizer tend to have more similar content. Based on this observation, we present a content-venue-aware topic model (CVTM) to capture group interests on an event from two perspectives: content and venue. The correlation between organizer and content is modeled in CVTM to alleviate the sparsity of textual content, and then we can further extract group interests on content of an event more accurately. Finally, a group event recommendation method using CVTM is proposed. We conduct comprehensive experiments to evaluate the recommendation performance of our model on two real-world datasets. The results demonstrate that the proposed model outperforms the state-of-the-art methods that suggest upcoming events for groups. Besides, CVTM can learn semantically coherent latent topics which are useful to explain recommendations. [ABSTRACT FROM AUTHOR]