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CBR-based Recommender Systems for Research Topic Finding

More and more universities and research institutes are taking scientific research as one of the main objectives. Finding a valuable research topic is an important activity that scientific researchers are more concerned about. But the existing Recommender Systems for Research Topic Finding that only provide simply document mining don’t satisfy the need of the researchers. This paper explores a new type of Recommender Systems for Research Topic Finding based on the CBR technology. We propose a framework of CBR-based Recommender Systems for Research Topic Finding (CRSRTF). Furthermore, the edit distance is used to solve a basic problem — knowledge representation and rule mining of the senior researchers’ experience.