Linear Regression Feature and Frog Leaping Algorithm based Web Page Recommendation
Pavithra. B.1, Niranjananmurthy M2

1Pavithra. B., Research Scholar, Department of Computer Applications, M S Ramaiah Institute of Technology, (Affiliated to Visvesvaraya Technological University, Karnataka), Bangalore (Karnataka), India.
2Dr. Niranjananmurthy M, Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management (Affiliated to Visvesvaraya Technological University, Karnataka), Bangalore (Karnataka), India.
Manuscript received on 30 November 2022 | Revised Manuscript received on 07 December 2022 | Manuscript Accepted on 15 December 2022 | Manuscript published on 30 December 2022 | PP: 32-37 | Volume-12 Issue-1, December 2022 | Retrieval Number: 100.1/ijitee.A93811212122 | DOI: 10.35940/ijitee.A9381.1212122
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Abstract: Website content and services attract surfers to visit page. Random visitor or first time visitor need more user suggestion for increasing the retaining of user. This work has worked in field of web page prediction as per user previous visits. Web mining logs and content features were further processed to extract the linear regression feature from the work. Extracted features were used for the page prediction in testing phase. Frog leaping genetic algorithm was used for the population generation and possible page prediction. Experiment was done on real dataset extracted from website. Results were compared with existing page prediction models and it was obtained that Web Page Prediction Frog Leaping Algorithm (WPPFLA) model has improved the work performance with respect to precision value, accuracy, Fitness measure and Metric values. 
Keywords: Information Extraction, Weblog, Neural Network, Regression, Recommendation
Scope of the Article: Neural Network