Web Page Recommendation System Research Articles

Deep Fuzzy Clustering and Deep Residual Network for Prediction of Web Pages from Weblog Data with Fractional Order Based Ranking

Web page recommendation system has attracted more attention in recent decades. The web page recommendation has various characteristics than the classical recommenders. It is the process of predicting the request of the next web page that users are significantly interested while searching the web. It helps the users to find relevant pages in the field of web mining. In particular, web user may spend more time to identify expected information. To understand behavior of users and to visit the page based on their interest at a specific time, an effective web page recommendation method is developed by developed Multi-Verse Sailfish Optimization (MVSFO)-based Deep Residual network. Accordingly, proposed MVSFO is derived by the integration of Multi-Verse Optimizer (MVO) and Sailfish Optimizer (SFO), respectively. Here, the process of recommendation is carried out using weblog data and the web page image. The sequential patterns are acquired from weblog data, and the patterns are grouped with Deep fuzzy clustering based on cosine similarity. The matching process among test pattern and sequential patterns are made using Canberra distance. Here, the recommended web pages obtained from the weblog data and pages obtained from web pages image using the Deep Residual network are enable to generate the output using fractional order-based ranking. The developed scheme attained more effectiveness by the measures, such as F-measure, precision, and recall as 85.30%, 86.59%, and 86.04%, respectively for MSNBC dataset.

Web Page Recommendation System Based on Text and Image Pattern Extraction Classification Model

Web Page Recommendation System Based on Text and Image Pattern Extraction Classification Model

Taylor Horse Herd Optimized Deep Fuzzy clustering and Laplace based K-nearest neighbor for web page recommendation

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.

Web page recommendation system using bat optimization and weighted support vector machine algorithm for health care service

Web-page recommendation commands a predominant part in smartweb systems. Discovery of meaningfulinformation from web utilization data and improvedrepresentation of knowledge for resourcefulweb-page recommendations are important and a huge challenge. The availabletechniques faces problems with information over load when the users tryretrieving the exact information that end users require. In addition,it also experiences issues with meaningful semantic information and thereforeit does not yield satisfactory web based services for the end users. To deal with the above statedissues, in this research work, BAOs (Bat Agent Optimizations) and WSVMs (Weighted Support Vector Machines) are combined to form BAO+WSVM algorithm for web page recommendations that are effective and useful. The experiments are carried out on the health records of COPDs (chronic obstructive pulmonary diseases) collected from websites of government hospitals. Attention has been paid on BAO+WSVM to providetheoptimal webpages for patients. User bat agent forms the interface between the user and the system providing an active environment for the users. At first, web log pre-processing is carried out with the help of data cleaning, user identification, and session identification. Later, Semantic information extractions are carried out applying DOWLs (Domain OntologyWeb language) which helps construct the ontology associated with a website utilizing its structure.

Intelligent and Adaptive Web Page Recommender System

In this manuscript, an intelligent and adaptive web page recommender system is proposed that provides personalized, global, and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: uniformity and recommendation strength. The system continuously tracks the user's responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset, which is a significant improvement over the 70% F1 measure reported by automatic clustering-based genetic algorithm, the prior web recommender system.

Webpage Recommendation System Based on the Social Media Semantic Details of the Website

The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection using Kullback-Leibler Divergence method. In this paper, socio semantic information is included with the similarity score which improves the recommendation accuracy. The social information from the social websites such as twitter is considered for web page recommendation. Initially number of web pages is collected and the similari-ty between web pages is computed by comparing their semantic information. The term frequency and inverse document frequency (tf-idf) is used to produce a composite weight, the most important terms in the web pages are extracted. Then the Pointwise Mutual Information (PMI) between the most important terms and the terms in the twitter dataset are calculated. The PMI metric measures the closeness between the twitter terms and the most important terms in the web pages. Then this measure is added with the similarity score matrix to provide the socio semantic search information for recommendation generation. The experimental results show that the pro-posed method has better performance in terms of prediction accuracy, precision, F1 measure, R measure and coverage.

An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

Semantic Variable Length Markov Chain Length Model (SVLMC) is a web page recommendation system which combined the fields of semantic web and web usage mining by the Markov transition probability matrix with rich semantic information extracted from web pages. Though it has high prediction accuracy, it has problem of high state space complexity. The high space complexity reduce the execution speed and reduce the performance of the system, which was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) model that provides high user satisfied recommendation and Confidence Pruned Markov Model (CPMM). The time consumption of CPMM was reduced by Support Vector Machine (SVM). But still the recommendation accuracy is still below the user satisfaction. So in this paper, quickest change detection using Kullback-Leibler Divergence method is introduced to improve the accuracy of recommendation generation by developing a scalable quickest change detection schemes that can be implemented recursively in a more complicated scenario of Markov model and it is included in the training data of SVM. Then the performance of web page recommendation is improved by ranking the web pages using page ranking technique. Thus the performance of web page recommendation generation system has been improved. The experiments are conducted to prove the effectiveness of the proposed work in terms of prediction accuracy, precision, recall, F1-measure, coverage and R measure.

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

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.

A novel approach on Particle Agent Swarm Optimization (PASO) in semantic mining for web page recommender system of multimedia data: a health care perspective

Recent decades have seen huge amounts of information collected in clinical databases for mining patients’ health states from multimedia data. Since on the web diverse type of web recommendation is made obtainable toward user every day with the purpose of consists of Image, Video, Audio, query suggestion and web page. Therefore, multimedia data accessible designed for patient-oriented decision making has improved significantly however is often scattered across various sites. In this perspective, Web page recommender systems with multimedia data might provide patients with further laymen-friendly information helping toward enhanced understand their health status as represented by their record. In this research work, Web Page health recommender systems are introduced via the use of certain agents in order to provide extremely appropriate web pages for patients. The main feature of Particle Agent Swarm Optimization (PASO) is that the creation of the algorithm is denoted by a set of Particle agents who cooperate in attaining the objective of the task under consideration. In the research method, two kinds of agents are presented: web user particle agent and semantic particle agent. PASO Based Web Page Recommendation (PASO-WPR) system is an intermediate program (or a particle agent) containing a user interface, which wisely produces a collection of info that suits an individual’s requirements. PASO-WPR is carried out dependent upon incorporating semantic info with data mining techniques on the web usage data as well as clustering of pages dependent upon similarity in their semantics. As the Web pages with multimedia files are viewed as ontology individuals, the pattern of patients’ navigation are like instances of ontology rather than the uniform resource locators, and with the help of semantic similarity, page clustering is carried out. For producing web page recommendations to users, the outcome is utilized. The recommender engine concentrates on the semantic info and as well exploits a particle agent to reform the outcomes of web pages recommendation. Consequently, the system response time is enhanced and as a result, creating the framework scalable. The outcomes recommend that the PASO-WPR system is improved in identifying the web page that a user is about to request while matched up other approaches. The outcome proves that the presented PASO-WPR system is carried out well in regard to the accuracy measures aspects such as accuracy, coverage and M-Metric are identified to contain greater values compared to the previous item based collaborative filtering recommendation systems.

An Improved Web Page Recommendation Technique for Better Surfing Experience

Recommendation systems are growing very rapidly. While surfing, users frequently miss the goal of their search and lost in information overload problem. To overcome this information overload problem, the authors have proposed a novel web page recommendation system to save surfing time of user. The users are analyzed when they surf through a particular web site. Authors have used relationship matrix and frequency matrix for effectively finding the connectivity among the web pages of similar users. These webpages are divided into various clusters using enhanced graph based partitioning concept. Authors classify active users more accurately to found clusters. Threshold values are used in both clustering and classification stages for more appropriate results. Experimental results show that authors get around 61% accuracy, 37% coverage and 46% F1 measure. It helps in improved surfing experience of users.

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

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.

A hybridized semantic trust-based framework for personalized web page recommendation

ABSTRACT The World Wide Web is constantly evolving and is the most dynamic information repository in the world that has ever existed. Since the information on the web is changing continuously and owing to the presence of a large number of similar web pages, it is very challenging to retrieve the most relevant information. With a large number of malicious and fake web pages, it is required to retrieve Web Pages that are trustworthy. Personalization of the recommendation of web pages is certainly necessary to estimate the user interests for suggesting web pages as per their choices. Moreover, the Web is tending towards a more organized Semantic Web which primarily requires semantic techniques for recommending the Web Pages. In this paper, a framework for personalized web page recommendation based on a hybridized strategy is proposed. Web Pages are recommended based on the user query by analyzing the Web Usage Data of the users. An array of strategies is intelligently integrated together to achieve an efficient Web Page Recommendation system. Latent Semantic Analysis is applied to the User-Term Matrix and the Term-Frequency Matrix that are built from the Web Usage Information to form a Term Prioritization Vector. Further, techniques like Latent Dirichlet Allocation for Topic-based Segregation of the URLs and Normalized Pointwise Mutual Information strategies are used for recommending web pages based on users’ queries. The Personalization is achieved by prioritizing the Web pages based on the Prioritization Vector. Also, a unique methodology is incorporated into the system to retrieve trustworthy websites. An overall Accuracy of 0.84 is achieved which is better than the existing strategies.

Prediction of User Future Request Utilizing the Combination of Both ANN and FCM in Web Page Recommendation

Abstract This paper explains about the web page recommendation system. This procedure encompasses consumers’ upcoming demand and web page recommendations. In the proposed web page recommendation system, potential and non-potential data can be categorized by use of the Levenberg–Marquardt firefly neural network algorithm, and forecast can be made by using the K-means clustering algorithm. Consequently, the projected representation demonstrates the infrequent contact format with the help of the representation that integrates the comparable consumer access model data that belong to the further consumer. Thereafter, the impending user data are specified to the clustering progression. The third phase of the projected process is collecting potential data with the aid of the improved fuzzy C-means clustering algorithm. The last step of our projected process is envisaging the upcoming demand for the subsequent consumer. The presentation of the projected procedure will be compared to the obtainable procedure.

Web page recommendation system based on partially ordered sequential rules

Web page recommendation system based on partially ordered sequential rules

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

With the exponential development of the number of users browsing the internet, an important factor that now the developer community is focussing on is the user experience. Recommender systems are the platforms that make personalized recommendations for a particular user by predicting the ratings for various items. Recommender systems majorly ignore the sequential information and rather focus on content information, but sequential information also provides much information about the behavior of the user. In this research work, we have presented a novel web-based recommender system which is based on sequential information of user’s navigation on web pages. We received top-N clusters when Fuzzy C-mean (FCM) clustering is employed. We determined the similar users for the target user and also evaluated the weight for each web page. We have tried to solve that problem of recommender systems as we offered a system to forecast a user’s next Web page visit. In our work, we proposed a system which generates recommendations to the users, by considering the sequential information that exists in their usage patterns of Web pages. We employed fuzzy clustering to give recommender system a sequential approach. We calculated weights for each page category considered in our system and predict top page recommendation for the target user. The real-world dataset of MNSBC is used in the experiments. The dataset consists of 5000 user entries with 6, entries per user. When we performed a comparison between the existing model with our proposed model, then it clearly showed that the accuracy of the proposed model is almost three times better than some existing systems. The accuracy of our proposed model is nearly 33 %.

Uncertainty Analysis for the Keyword System of Web Events

Webpage recommendations for hot Web events can assist people to easily follow the evolution of these Web events. At the same time, there are different levels of semantic uncertainty underlying the amount of Webpages for a Web event, such as recapitulative information and detailed information. Apparently, the grasp of the semantic uncertainty of Web events could improve the satisfactoriness of Webpage recommendations. However, traditional hit-rate-based or clustering-based Webpage recommendation methods have overlooked these different levels of semantic uncertainty. In this paper, we propose a framework to identify the different underlying levels of semantic uncertainty in terms of Web events, and then utilize these for Webpage recommendations. Our idea is to consider a Web event as a system composed of different keywords, and the uncertainty of this keyword system is related to the uncertainty of the particular Web event. Based on keyword association linked network Web event representation and Shannon entropy, we identify the different levels of semantic uncertainty, and construct a semantic pyramid (SP) to express the uncertainty hierarchy of a Web event. Finally, an SP-based Webpage recommendation system is developed. Experiments show that the proposed algorithm can significantly capture the different levels of the semantic uncertainties of Web events and it can be applied to Webpage recommendations.

User Next Web Page Recommendation using Weight based Prediction

The World Wide Web is a source of knowledge; the knowledge is extracted from the web data. Web data is available in direct from normal web as contents to user and/or in direct forms to as the web access logs. For the web usage pattern analysis the web access logs are analysed. Web usage data used in various applications of web masters, user data recommendations, web pre-fetching and caching. In this paper using the web access log analysis, web next page recommendation system is introduced. The presented technique involves data personalization, user behavioural analysis and access patterns for recommendations. The proposed web page recommendation system contains the K-means algorithm for finding similar access patterns of the user sessions. Additionally for classification and prediction the KNN algorithm is implemented. The model also incorporate the similar user access pattern data which is belongs from the other user therefore the proposed model also predicts the rarely accessed patterns. Thus to make the recommendations web usages data is personalized, based on URL frequencies, user navigational frequencies, session based data analysis and time based data analysis. Additionally to combine these parameters a weighted technique is used. The proposed recommendation system is implemented using JAVA technology. And their performance in terms of accuracy, error rate, space complexity and time complexity is estimated. The experimentation with increasing amount of data provides more accurate results and also consumes less computational resources. Therefore the proposed data model is adoptable for accuracy and efficiency both.

Effective web personalisation based on rough biclustering

Web personalisation systems include a new generation of recommender systems that integrate multiple online channels, are more scalable, are more adaptive and can better handle user interactivity. Efficient and intelligent techniques from artificial intelligence, machine learning, web mining and statistics are needed to mine this data for actionable knowledge, and to effectively use the discovered knowledge to enhance the users’ experience. In this paper, we propose a rough biclustering approach for creating user model, based on which user profiles are constructed and the user profiles are matched with the active user session for web page recommendation. To determine the effectiveness of the proposed approach, it is compared with conventional biclustering, spectral co-clustering and CDK-means biclustering using the evaluation metric used for page recommendation. The experimental results show that the proposed rough biclustering algorithm outperforms the other approaches for web page recommender systems.

Combination of Web page recommender systems

With the rapid growth of the World Wide Web (www), finding useful information from the Internet has become a critical issue. Web recommender systems help users make decisions in this complex information space where the volume of information available to them is huge. Recently, a number of Web page recommender systems have been developed to extract the user behavior from the user’s navigational path and predict the next request as s/he visits Web pages. However, each of these systems has its own merits and limitations. In this paper, we investigate a hybrid recommender system, which combines the results of several recommender techniques based on Web usage mining. We conduct a detailed comparative evaluation of how different combined methods and different recommendation techniques affect the prediction accuracy of the hybrid recommender. We then discuss the results in terms of using a hybrid recommender system instead of a single recommender model. Our results suggest that the hybrid recommender system is better in predicting the next request of a Web user.

Anonymous P2P Web Browse History Sharing for Web Page Recommendation

This paper proposes a new method for realizing the web page recommendation system by sharing users' web browse history on an anonymous P2P network. Our scheme creates a user profile, a summary of the user's web browse trends, by analyzing the contents of the web pages browsed. The scheme then provides a P2P network to exchange web browse histories so as to create mutual web page recommendations. The novelty of our method lies in its P2P network formulation; it is formulated in a way so that users having similar user profiles are automatically connected, yet their user profiles are protected from being disclosed to other users. The proposed method intentionally distributes bogus user profiles on the P2P network, while not harming the efficiency of the web browse history sharing process.