An effective web page recommender system with fuzzy c-mean clustering

Katarya, Rahul; Verma, Om Prakash

doi:10.1007/s11042-016-4078-7

Cited by 52 publications

(26 citation statements)

References 59 publications

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“…R. Katarya, and O. P. Verma, [19] recommend the web-pages according to the sequential information of user's navigation by employing Fuzzy C-Mean (FCM) clustering for receiving the top-N clusters. The method found out a user's next Web page visit by identifying the similar users to target user.…”

Section: Literature Reviewmentioning

confidence: 99%

Web Page Recommendation using Random Forest with Fire Fly Algorithm in Web Mining

Mane,

Jetawat,

Nikumbh

2020

IJEAT

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Abstract: Nowadays, internet has become the easiest way to obtain more information from the web and millions of users search internet to find out the information. The continuous growth of web pages and users interest to search more information about various topics increases the complexity of recommendation. The user's behavior is extracted by using the web mining techniques, which are used in web server log. The main aim of this research study is to identify the navigation pattern of users from the log files. There are three major steps in the web mining process namely pre-processing the data, classification of pattern and users discovery. In recent periods, the web page articles are classified by the researchers before recommending the requested page to users. However, every category size is too large or manual labors are often needed for classification tasks. A high time complexity issues are faced by some existing clustering methods or according to the initial parameters, these techniques provides the iterative computing that leads to insufficient results. To address the above issues, a recommendation for web page is developed by initializing the margin parameters of classification techniques which considers both effectiveness and efficiency. This research work initializes the Random Forest's (RF) margin parameters by using the FireFly Algorithm (FFA) for reducing the processing time to speed up the process. A large volume of user's interest data is processed by these margin parameters, which provides a better recommendation than existing techniques. The experimental results show that RF-FFA method achieved 41.89% accuracy and recall values, when compared with other heuristic algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Web Page Recommendation using Random Forest with Fire Fly Algorithm in Web Mining

Mane,

Jetawat,

Nikumbh

2020

IJEAT

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“…R. Katarya, and O.P. Verma [13] illustrated a new web-based recommender system, which was based on the sequential information about user's navigation on the web pages. In this literature, Fuzzy C Means (FCM) clustering technique was used to provide the recommender system sequentially.…”

Section: Literature Reviewmentioning

confidence: 99%

Rapid Frequent Pattern Growth and Possibilistic Fuzzy C-means Algorithms for Improving the User Profiling Personalized Web Page Recommendation System

Sahoo¹,

Ratha²

2018

IJIES

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Web page recommendation systems are used to recommend the future web page views to World Wide Web (WWW) users. Existing personalized web page recommendation systems are still limited to several problems such as, cold start, sparse data structures (Sparsity), and no diversity in the set of recommended items (Content Overspecialization). In order to overcome these difficulties, we proposed a new personalized web recommendation system by considering the different contributions of the training samples (MSNBC dataset, and castings technology international dataset). At first, the web pages are converted into number of sequences. An effective clustering algorithm called as Possibilistic Fuzzy C-Means (PFCM) is employed to identify the neighbourhood of each user profile. Then the association rule mining technique, Rapid Frequent Pattern Growth (RFPG) algorithm is used for mining the frequent web pages. Finally, the experimental outcome shows that the proposed approach delivers the high priority of web pages and also recommends the related web pages. Finally, the experimental outcome shows that the proposed approach improved accuracy in web page recommendation up to 50-60% compared to the existing methods.

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“…Also, they had made use of several web mining strategies to identify the useful patterns from the log files. Duwairi, R and Ammari, H [5] presented the modified version of the enhanced Cluster-based Association Rule Mining (CBAR) algorithm for recommending the webpage. The technique performed the recommendation online and hence, allowed dynamic updates.…”

Section: Literature Surveymentioning

confidence: 99%

“…  (5) where, j M indicates the th j sequential pattern and it has k subsequences. The subsequences in the sequential patterns is considered to be a subset of webpage and it is represented as H M j  .…”

Section: Sequential Pattern Mining Based On the Prefix Span Algorithmmentioning

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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An effective web page recommender system with fuzzy c-mean clustering

Cited by 52 publications

References 59 publications

Web Page Recommendation using Random Forest with Fire Fly Algorithm in Web Mining

Web Page Recommendation using Random Forest with Fire Fly Algorithm in Web Mining

Rapid Frequent Pattern Growth and Possibilistic Fuzzy C-means Algorithms for Improving the User Profiling Personalized Web Page Recommendation System

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

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