Abstract
Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.
Highlights
The overabundance of information that is available over the internet makes information seeking a difficult task
Different from the existing works, in this paper, we propose a new approach based on collaborative filtering that utilizes only publicly available contextual metadata to personalize
We utilized the publicly available contextual metadata to leverage the advantages of collaborative filtering approach in recommending a set of related papers to a researcher based on paper-citation relations
Summary
The overabundance of information that is available over the internet makes information seeking a difficult task. Researchers find it difficult to access and keep track of the most relevant and promising research papers of their interest [1]. The results from this approach largely depend on how good the user is in fine-tuning the query message beside its inability to personalize the searching results. Another classical approach used by most researchers is to follow the list of references from the documents they already possessed [3].
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