Hybrid Data Aggregation Technique to Categorize the Web Users to Discover Knowledge About the Web Users

Manohar, E.; Punithavathani, D. Shalini

doi:10.1007/s11277-017-4779-x

Cited by 3 publications

(1 citation statement)

References 26 publications

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“… One of the major challenges faced by the recommendation systems is that the web users are not aware of the credibility of the webpage suggested, as more websites pay the search engines to increase their respective ranks. Recommending this webpage may result in losing the customer satisfaction [9].…”

Section: Challengesmentioning

confidence: 99%

Web Page Recommendation System Using Laplace Correction Dependent Probability and Chronological Dragonfly-Based Clustering

Jadhav¹,

Arunkumar²

2018

IJET

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The web page recommenders predict and recommend the web pages to the users based on the behavior of their search history. The web page recommender system analyzes the semantics of the navigation by the user and predicts the related web pages for the user. Various recommender systems have been developed in the literature for the web page recommendation. In the first work, a web page recommendation system was developed using weighted sequential pattern mining and Wu and Li Index Fuzzy clustering (WLI-FC) algorithm. In this work, the Chronological based Dragonfly Algorithm (Chronological-DA) is proposed for recommending the webpage to the users. The proposed Chronological-DA algorithm includes the concept of the chronological for recommending the webpage based on the history of pages visited by the users. Also, the proposed recommendation system uses the concept of Laplacian correction for defining the recommendation probability. Simulation of the proposed webpage recommendation system with the chronological-DA uses the standard CTI and the MSNBC database for the experimentation, and the experimental results prove that the proposed scheme has better values of 1, 0.964, and 0.973 for precision, recall, and F-measure respectively.

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Section: Challengesmentioning

confidence: 99%

Web Page Recommendation System Using Laplace Correction Dependent Probability and Chronological Dragonfly-Based Clustering

Jadhav¹,

Arunkumar²

2018

IJET

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Webpage Recommendation System Using Interesting Subgraphs and Laplace Based k-Nearest Neighbor

Jayalakshmi¹,

Padmaja

Suma

2019

Int. J. Patt. Recogn. Artif. Intell.

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An interesting research area that permits the user to mine the significant information, called frequent subgraph, is Graph-Based Data Mining (GBDM). One of the well-known algorithms developed to extract frequent patterns is GASTON algorithm. Retrieving the interesting webpages from the log files contributes heavily to various applications. In this work, a webpage recommendation system has been proposed by introducing Chronological Cuckoo Search (Chronological-CS) algorithm and the Laplace correction based k-Nearest Neighbor (LKNN) to retrieve the useful webpage from the interesting webpage. Initially, W-Gaston algorithm extracts the interesting subgraph from the log files and provides it to the proposed webpage recommendation system. The interesting subgraphs subjected to clustering with the proposed Chronological-CS algorithm, which is developed by integrating the chronological concept into Cuckoo Search (CS) algorithm, provide various cluster groups. Then, the proposed LKNN algorithm recommends the webpage from the clusters. Simulation of the proposed webpage recommendation algorithm is done by utilizing the data from MSNBC and weblog database. The results are compared with various existing webpage recommendation models and analyzed based on precision, recall, and F-measure. The proposed webpage recommendation model achieved better performance than the existing models with the values of 0.9194, 0.8947, and 0.86736, respectively, for the precision, recall, and F-measure.

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Implementation of Customer Behavior Evaluation System Using Real-time Web Log Stream Data

Lee¹,

Park²,

Lee³

2018

jkiit

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Hybrid Data Aggregation Technique to Categorize the Web Users to Discover Knowledge About the Web Users

Cited by 3 publications

References 26 publications

Web Page Recommendation System Using Laplace Correction Dependent Probability and Chronological Dragonfly-Based Clustering

Web Page Recommendation System Using Laplace Correction Dependent Probability and Chronological Dragonfly-Based Clustering

Webpage Recommendation System Using Interesting Subgraphs and Laplace Based k-Nearest Neighbor

Implementation of Customer Behavior Evaluation System Using Real-time Web Log Stream Data

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