Taylor Horse Herd Optimized Deep Fuzzy clustering and Laplace based K-nearest neighbor for web page recommendation. (January 2023) - Jayalakshmi, N; Sangeeta, V; Muttipati, Appala Srinuvasu
Highlights: This research work is highly focused on web page recommendation. This research work is highly meaningful, as it effectively recommends the suitable web pages according to the user queries. Moreover, to know about the most interesting works undergone in literature, we have collected a total of 8 literature papers on web page recommendation concepts. All these papers were collected from 2019 to 2022 (i.e. 4 years). Analyzing the past as well as recent works helps to gain more knowledge regarding the web page recommendation. In addition, it's good to learn the advantage and drawbacks of existing works, which might be a mile stone for the future researchers. Abstract: Background: : Web-page recommendation serves a significant part in intelligent web frameworks and discovery of meaningful information from web and sufficient knowledge illustration for efficient web-page recommendations are quintessential and highly demanding issue. The conventional key matching and statistical models for web page recommendation are insufficient as they recommend web pages to the user mostly irrelevant to the query. It is essential to satisfy individual user separately according to the available preference data. In contrast, graph framework can deliver suitable illustration of user-item preference data and by employing this graph structure-based web page recommendation system is highly desirable and efficient. Methods: : This research proposes a web page recommendation model using Taylor Horse Herd Optimization (THHO)-based Deep Fuzzy Clustering (DFC). Here, the interesting sub graphs from web log database are retrieved using Weighted-Gaston (W-Gaston) algorithm. DFC is exploited to cluster the sub graphs and DFC is effectively trained using THHO algorithm. Moreover, THHO is devised by the incorporation of Taylor series with Horse Herd Optimization Algorithm (HOA). Finally, suitable web pages are recommended to the user based on their query using Laplace correction-based K-Nearest neighbor (LKNN) model. Result: : The proposed THHO_DFC approach has obtained maximum precision of 0.950, recall of 0.897, F-measure of 0.922, and accuracy of 0.926 while analyzing the system based on 90% data using MSNBC dataset. Conclusion: : The proposed model has delivered better insights in terms of efficiency and provides high performance in recommending suitable web pages.