# Multi-task recommendation system for scientific papers with high-way networks

arXiv: https://arxiv.org/abs/2204.09930 · HTML: https://mlsanity.com/paper/2204.09930/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [Ask the GRU: Multi-Task Learning for Deep Text Recommendations](https://mlsanity.com/paper/1609.02116/) (2016)
- [Scientific Paper Recommendation: A Survey](https://mlsanity.com/paper/2008.13538/) (2020)
- [Learning text representation using recurrent convolutional neural network with highway layers](https://mlsanity.com/paper/1606.06905/) (2016)

## Abstract (arXiv)

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.