  Attributes, such as metadata and profile, carry useful information which in
principle can help improve accuracy in recommender systems. However, existing
approaches have difficulty in fully leveraging attribute information due to
practical challenges such as heterogeneity and sparseness. These approaches
also fail to combine recurrent neural networks which have recently shown
effectiveness in item recommendations in applications such as video and music
browsing. To overcome the challenges and to harvest the advantages of sequence
models, we present a novel approach, Heterogeneous Attribute Recurrent Neural
Networks (HA-RNN), which incorporates heterogeneous attributes and captures
sequential dependencies in \textit{both} items and attributes. HA-RNN extends
recurrent neural networks with 1) a hierarchical attribute combination input
layer and 2) an output attribute embedding layer. We conduct extensive
experiments on two large-scale datasets. The new approach show significant
improvements over the state-of-the-art models. Our ablation experiments
demonstrate the effectiveness of the two components to address heterogeneous
attribute challenges including variable lengths and attribute sparseness. We
further investigate why sequence modeling works well by conducting exploratory
studies and show sequence models are more effective when data scale increases.
