Cross-Domain Academic Paper Recommendation by Semantic Linkage Approach Using Text Analysis and Recurrent Neural Networks

摘要

In this digital age, free-flow and exchange of knowledge and information are of paramount importance. This is the prime reason why we decided to tackle cross-domain linkage. Firstly, we build a system which recommends scholarly academic papers based on the content of news article a user is reading using text analysis techniques. We perform a human expert evaluation to test the system for relevance. Our judges show good agreement with a kappa value of 0.869. To improve the quality of recommendations further, we use an RNN-LSTM model trained on Wikipedia to measure document relevance. We reorder a list of academic papers based on their semantic similarity with the input document using our RNN-LSTM model. Our model achieves a slightly better performance than one of the best document embedding techniques doc2vec (paragraph vector). To the best of our knowledge, ours is the first study linking the domains of News Media and Academic landscape, and bridging the knowledge-gap.