A Novel Approach for Personalized Article Recommendation in Online Scientific Communities

Rapid proliferation of information technologies has generated sheer volume of information which makes scientific research related information searching more challenging. Personalized recommendation is the widely adopted technique to recommend relevant documents to researchers. Current methods are suffering from mismatch problem and match irrelevance problem and fail to generate highly related results. To overcome these problems, we propose a novel approach to recommend articles to the researchers. In our approach we integrate three types of similarity measures: keyword similarity, journal similarity, and author similarity to measure the relevance of the articles to researchers. The keyword similarity is used to generate candidate list of articles, and the journal similarity and author similarity are used to select most suitable articles from the candidate list. The integrated similarity measure is used to rank the articles based on their relevance. The proposed method is implemented in Scholar Mate (www.scholarmate.com), the online research social network platform. The evaluation results exhibit that proposed method is more effective than existing ones.