  Sequential recommendation is a fundamental task for network applications, and
it usually suffers from the item cold start problem due to the insufficiency of
user feedbacks. There are currently three kinds of popular approaches which are
respectively based on matrix factorization (MF) of collaborative filtering,
Markov chain (MC), and recurrent neural network (RNN). Although widely used,
they have some limitations. MF based methods could not capture dynamic user's
interest. The strong Markov assumption greatly limits the performance of MC
based methods. RNN based methods are still in the early stage of incorporating
additional information. Based on these basic models, many methods with
additional information only validate incorporating one modality in a separate
way. In this work, to make the sequential recommendation and deal with the item
cold start problem, we propose a Multi-View Recurrent Neural Network (MV-RNN})
model. Given the latent feature, MV-RNN can alleviate the item cold start
problem by incorporating visual and textual information. First, At the input of
MV-RNN, three different combinations of multi-view features are studied, like
concatenation, fusion by addition and fusion by reconstructing the original
multi-modal data. MV-RNN applies the recurrent structure to dynamically capture
the user's interest. Second, we design a separate structure and a united
structure on the hidden state of MV-RNN to explore a more effective way to
handle multi-view features. Experiments on two real-world datasets show that
MV-RNN can effectively generate the personalized ranking list, tackle the
missing modalities problem and significantly alleviate the item cold start
problem.
