Abstract
The obligation for research paper recommendation is high as scientific document recommendations like research paper recommendation frameworks are not many in number. The knowledge-centric semantically inclined framework for research paper recommendation HyResPR has been proposed. HyResPR is a hybridized research paper recommendation model which is implemented on the RARD II dataset. A hybridized research paper recommendation framework (HyResPR) is proposed. It uses user queries as input to obtain the query words after preprocessing, later these query words are induvial integrated with the domain ontologies. The dataset is subjected to preprocessing to create a synthesized knowledge map with the help of category term mapping and static domain ontology alignment. Features are extracted from the synthesized knowledge map and classified using logistic regression, the extracted query words are processed along with the top 75% of the instances classified. The experimentations are conducted on the RARD II dataset which is classified using the logistic regression classifier. The SemantoSim similarity measure and Cosine similarity measures are used to compute the similarity among the extracted instances from RARD II dataset. The query words obtained from input, and relevant research papers are recommended back to user depending on the value of the semantic similarity. Auxiliary knowledge is incorporated by using static domain ontology, topic modeling has been used experimentations have been computed for 1416 queries on the RARD II dataset. The proposed HyResPR framework achieved the highest average accuracy of 96.43%, recall of 97.05%, with a least FDR value of 0.05 observed.
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Maddineni, S., Deepak, G., Praveen, S.V. (2023). HyResPR: Hybridized Framework for Recommendation of Research Paper Using Semantically Driven Machine Learning Models. In: Sharma, S., Subudhi, B., Sahu, U.K. (eds) Intelligent Control, Robotics, and Industrial Automation. RCAAI 2022. Lecture Notes in Electrical Engineering, vol 1066. Springer, Singapore. https://doi.org/10.1007/978-981-99-4634-1_66
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