An Article Recommendation Technique from a Multi-Layer Reference Article Graph for Facilitating Chronological Learning

With the rapid growth of scientific publications, researchers often find difficulty in discovering appropriate articles that can mitigate the knowledge gaps to understand a target article (a.k.a., base article in this paper). In this case, reference articles can play an important role. It may happen that a researcher may have to read several levels of references, which is challenging since it increases exponentially over levels. This kind of learning method could be considered as the chronological learning. In this paper, a chronological learning supported recommender system is proposed, which utilizes the reference articles of multiple levels for generating a multi-level weighted graph. The weights of the various nodes in this graph are calculated considering three scores, namely lexical similarity, time-aware influence, and node centrality. Among them, the equation for calculating the time-aware influence score is improved by taking citation counts into consideration so that the articles with the higher citation counts receive higher influence scores, which is more practical. From this graph, a chronological path is selected considering a weight-based selection process envisioning mitigating the knowledge gaps to understand a base article. Since the keyphrase extraction plays an important role in this system, various unsupervised keyphrase extraction techniques are evaluated to discover the most suitable relevant technique for the proposed system.