An Efficient Mining for Recommendation System for Academics
Nikhat Akhtar1, Devendera Agarwal2
1Nikhat Akhtar, Research Scholar Ph.D (Computer Science & Engineering), Babu Banarasi Das University, Lucknow, India.
2Prof. (Dr.) Devendera Agarwal, Professor, Babu Banarasi Das University, Lucknow, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1619-1626 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5924018520/2020©BEIESP | DOI: 10.35940/ijrte.E5924.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: At present time huge numbers of research articles are available on World Wide Web in any domain. The research scholar explores a research papers to get the appropriate information and it takes time and effort of the researcher. In this scenario, there is the need for a researcher to search a research based on its research article. In the present paper a method of Knowledge ablation from a collection of research articles, is presented to evolve a system research paper recommendation system (RPRS), which would generate the recommendations for research article based on researcher choice. The RPRS accumulate the knowledge ablated from the pertinent research articles in the form of semantic tree. It accumulates all the literal sub parts with their reckoning in nodes. These parts are arranged based on their types in such a way that the leaf nodes stores the words with its prospect, the higher layer gives details about dictum with its reckoning, next to it an abstract. A Bayesian network is applied to construct a verisimilitude model which would quotation the pertinent tidings from the knowledge tree to construct the recommendation and word would be scored through TF-IDF value.
Keywords: Bayesian Network, Text Clustering, Knowledge Extraction, Text Classification, Recommender System.
Scope of the Article: Classification.