DOTNET PROJECT
A
Hierarchical Attention Model for Social Contextual Image Recommendation
Abstract:
Image based social
networks are among the most popular social networking services in recent years.
With tremendous images uploaded everyday, understanding users' preferences to
the 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 for enhancing recommendation performance. Nevertheless, these
previous works failed to capture the unique characteristics of social image
platforms or relied on predefined weights in combining different kinds of
information. 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 coherence, 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. Finally, extensive experimental results on real-world
datasets clearly show the superiority of our proposed model.
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