  Context-aware recommender systems (CARSs) apply sensing and analysis of user
context in order to provide personalized services. Adding context to a
recommendation model is challenging, since the addition of context may
increases both the dimensionality and sparsity of the model. Recent research
has shown that modeling contextual information as a latent vector may address
the sparsity and dimensionality challenges. We suggest a new latent modeling of
sequential context by generating sequences of contextual information and
reducing their contextual space to a compressed latent space.We train a long
short-term memory (LSTM) encoder-decoder network on sequences of contextual
information and extract sequential latent context from the hidden layer of the
network in order to represent a compressed representation of sequential data.
We propose new context-aware recommendation models that extend the neural
collaborative filtering approach and learn nonlinear interactions between
latent features of users, items, and contexts which take into account the
sequential latent context representation as part of the recommendation process.
We deployed our approach using two context-aware datasets with different
context dimensions. Empirical analysis of our results validates that our
proposed sequential latent context-aware model (SLCM), surpasses state of the
art CARS models.

Machine Learning;Machine Learning