  Finding and selecting the most relevant scientific papers from a large number
of papers written in a research community is one of the key challenges for
researchers these days. As we know, much information around research interest
for scholars and academicians belongs to papers they read. Analysis and
extracting contextual features from these papers could help us to suggest the
most related paper to them. In this paper, we present a multi-task
recommendation system (RS) that predicts a paper recommendation and generates
its meta-data such as keywords. The system is implemented as a three-stage deep
neural network encoder that tries to maps longer sequences of text to an
embedding vector and learns simultaneously to predict the recommendation rate
for a particular user and the paper's keywords. The motivation behind this
approach is that the paper's topics expressed as keywords are a useful
predictor of preferences of researchers. To achieve this goal, we use a system
combination of RNNs, Highway and Convolutional Neural Networks to train
end-to-end a context-aware collaborative matrix. Our application uses Highway
networks to train the system very deep, combine the benefits of RNN and CNN to
find the most important factor and make latent representation. Highway Networks
allow us to enhance the traditional RNN and CNN pipeline by learning more
sophisticated semantic structural representations. Using this method we can
also overcome the cold start problem and learn latent features over large
sequences of text.
