Abstract
The ever-evolving nature of research works creates the cacophony of new topics incessantly resulting in an unstable state in every field of research. Researchers are disseminating their works producing a huge volume of articles. In fact, the spectacular growth in scholarly literature is widening the choice sets overwhelmingly for researchers. Consequently, they face difficulties in identifying a suitable topic of current importance from a plethora of research topics. This remains an ill-defined problem for researchers due to the overload of choices. The problem is even more severe for new researchers due to the lack of experience. Hence, there is a definite need for a system that would help researchers make decisions on appropriate topics. Recommender systems are good options for performing this very task. They have been proven to be useful for researchers to keep pace with research dynamics and at the same time to overcome the information overload problem by retrieving useful information from the large information space of scholarly literature. In this article, we present RTRS, a knowledge-based Research Topics Recommender System to assist both novice and experienced researchers in selecting research topics in their chosen field. The core of this system hinges upon bibliometric information of the literature. The system identifies active research topics in a particular area and recommends top N topics to the target users. The results obtained have proven useful to academic researchers, particularly novices, in making an early decision on research topics.
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Acknowledgements
We warmly thank our colleagues for their valuable support and assistance. This research is supported by UM Research Grant No. RP028B-14AET.
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Alam, M.M., Ismail, M.A. RTRS: a recommender system for academic researchers. Scientometrics 113, 1325–1348 (2017). https://doi.org/10.1007/s11192-017-2548-y
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DOI: https://doi.org/10.1007/s11192-017-2548-y