1 research outputs found
Learning Preferences and Demands in Visual Recommendation
Visual information is an important factor in recommender systems, in which
users' selections consist of two components: \emph{preferences} and
\emph{demands}. Some studies has been done for modeling users' preferences in
visual recommendation. However, conventional methods models items in a common
visual feature space, which may fail in capturing \emph{styles} of items. We
propose a DeepStyle method for learning style features of items. DeepStyle
eliminates the categorical information of items, which is dominant in the
original visual feature space, based on a Convolutional Neural Networks (CNN)
architecture. For modeling users' demands on different categories of items, the
problem can be formulated as recommendation with contextual and sequential
information. To solve this problem, we propose a Context-Aware Gated Recurrent
Unit (CA-GRU) method, which can capture sequential and contextual information
simultaneously. Furthermore, the aggregation of prediction on preferences and
demands, i.e., prediction generated by DeepStyle and CA-GRU, can model users'
selection behaviors more completely. Experiments conducted on real-world
datasets illustrates the effectiveness of our proposed methods in visual
recommendation