Web Page Recommendation Research Articles

E-commerce Webpage Recommendation Scheme Base on Semantic Mining and Neural Networks

In e-commerce websites, web mining web page recommendation technology has been widely used. However, recommendation solutions often cannot meet the actual application needs of online shopping users. To address this problem, this paper proposes an e-commerce web page recommendation solution that combines semantic web mining and BP neural networks. First, the web logs of user searches are processed and 5 features are extracted: content priority, time consumption priority, online shopping users' explicit/implicit feedback on the website, recommendation semantics and input deviation amount. Then, these features are used as input features of the BP neural network to classify and identify the priority of the final output web page. Finally, the web pages are sorted according to priority and recommended to users. This project uses book sales webpages as samples for experiments. The results show that this solution can quickly and accurately identify the webpages required by users.

High-Dimensional Dueling Optimization with Preference Embedding

In many scenarios of black-box optimization, evaluating the objective function values of solutions is expensive, while comparing a pair of solutions is relatively cheap, which yields the dueling black-box optimization. The side effect of dueling optimization is that it doubles the dimension of solution space and exacerbates the dimensionality scalability issue of black-box optimization, e.g., Bayesian optimization. To address this issue, the existing dueling optimization methods fix one solution when dueling throughout the optimization process, but it may reduce their efficacy. Fortunately, it has been observed that, in recommendation systems, the dueling results are mainly determined by the latent human preferences. In this paper, we abstract this phenomenon as the preferential intrinsic dimension and inject it into the dueling Bayesian optimization, resulting in the preferential embedding dueling Bayesian optimization (PE-DBO). PE-DBO decouples optimization and pairwise comparison via the preferential embedding matrix. Optimization is performed in the preferential intrinsic subspace with much lower dimensionality, while pairwise comparison is completed in the original dueling solution space. Theoretically, we disclose that the preference function can be approximately preserved in the lower-dimensional preferential intrinsic subspace. Experiment results verify that, on molecule discovery and web page recommendation dueling optimization tasks, the preferential intrinsic dimension exists and PE-DBO is superior in scalability compared with that of the state-of-the-art (SOTA) methods.

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

Automatic Clustering of User Behaviour Profiles for Web Recommendation System

Web usage mining, content mining, and structure mining comprise the web mining process. Web-Page Recommendation (WPR) development by incorporating Data Mining Techniques (DMT) did not include end-users with improved performance in the obtained filtering results. The cluster user profile-based clustering process is delayed when it has a low precision rate. Markov Chain Monte Carlo-Dynamic Clustering (MC2-DC) is based on the User Behavior Profile (UBP) model group’s similar user behavior on a dynamic update of UBP. The Reversible-Jump Concept (RJC) reviews the history with updated UBP and moves to appropriate clusters. Hamilton’s Filtering Framework (HFF) is designed to filter user data based on personalised information on automatically updated UBP through the Search Engine (SE). The Hamilton Filtered Regime Switching User Query Probability (HFRSUQP) works forward the updated UBP for easy and accurate filtering of users’ interests and improves WPR. A Probabilistic User Result Feature Ranking based on Gaussian Distribution (PURFR-GD) has been developed to user rank results in a web mining process. PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various methods by using the Gaussian Distribution Function (GDF). The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%. HFRSUQP enabled extensive Maximize Log-Likelihood (ML-L) increases to 15.28% of User Personalized Information Search Retrieval Rate (UPISRT). For feature ranking, the PURFR-GD model defines higher Classification Accuracy (CA) and Precision Ratio (PR) while utilising minimum Execution Time (ET). Furthermore, UPISRT's ranking performance has improved by 20%.

Bi-LSTM-AMSM: bidirectional long short-term memory network and attention mechanism with semantic mining for e-commerce web page recommendation

Bi-LSTM-AMSM: bidirectional long short-term memory network and attention mechanism with semantic mining for e-commerce web page recommendation

Bi-LSTM-AMSM: Bidirectional Long Short-term Memory Network and Attention Mechanism with Semantic Mining for E-commerce Web Page Recommendation

Bi-LSTM-AMSM: Bidirectional Long Short-term Memory Network and Attention Mechanism with Semantic Mining for E-commerce Web Page Recommendation

Linear Regression Feature and Frog Leaping Algorithm based Web Page Recommendation

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 projecttunnel.com 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.

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.

Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

SummaryWeb‐page recommendation system (RS) plays a major role in smart web systems. The core technology is the RS, which helps users find their interests or discover item preferences. With the rapid proliferation of information technology, the present era witnessed a remarkable growth in the collection and generation of web data. Every day, it is difficult to project the appropriate information to the user and adds severe complexity to the decision making procedure. Moreover, the existing RS techniques have limitations like huge computation time, high computational cost, over specialization, and sparsity problems. This work presents an effective graph‐based web page recommendation model to overcome these difficulties. Initially, the input data is preprocessed utilizing Natural Language Tool Kit executed in Python. The snapshot‐based dynamic graph model obtains the user‐web page relationship. Weight calculation of user‐item and the user‐user edge is also processed with this graph model. The user‐user edge similarity is used to obtain the user preference vector. Then, unsupervised‐deep auto encoder based density‐based clustering algorithm is proposed to cluster the user preference vector, which groups the visited web page details into k clusters. The web pages are recommended based on the highest score calculated by the page ranking method. Finally, the topmost visited web pages are recommended to web users based on the ranking score. “All the news”, MSNBC, and Weblog datasets are taken for evaluation. The proposed method is evaluated with respect to standard measures like F1 measure, accuracy, recall, precision, and root mean square error. Compared with other existing methods, the proposed technique achieved the highest accuracy, 97.99% for All the news datasets, 93.03% for the MSNBC database, and 90.07% for the Weblog database.

Classification of Web Pages Using the Machine Learning Algorithms with Web Page Recommendations

Classification of Web Pages Using the Machine Learning Algorithms with Web Page Recommendations

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.

Combination of data mining and artificial intelligence algorithms for efficient web page recommendation

Due to the obvious unstable increase in information, the web is saturated with data, which makes the data search a complicated task. Existing web-based recommendation systems include shortcomings such as a lack of capability as well as scalability when dealing with online data, and blockages created by traffic while utilising the website during peak hours. Web recommendation systems help consumers find the right content and make the information search process easier. Web usage mining is regarded as the primary source for web recommendation, and it is used in conjunction with association rule mining and the C4.5 algorithm to recommend online pages to the user. The Google search engine has been widely enhanced the likelihood on the system's suggested structure. A web log is created when a user enters a search query into a search engine. This query would be compared to the web logs by the proposed system. The associate rule mining technique helps in matching the user's search query to the online log. The C4.5 algorithm is linked to a priority based on reviews, which obviously ranks the search based on priority for greater validation result.

Analysis and Design of “Universitas Dinamika” Web Page Design in Creating Customer Experience and Buying Interest of New Students from Sidoarjo

The community's need for higher education in Indonesia continues to grow, seen from the increasing number of higher education institutions every year. Dinamika University as one of 328 Private Universities (PU) in East Java has implemented marketing tools using a website ( www.dinamika.ac.id ). However, the availability of the website has not been proven to be effective in attracting new students to register at Dinamika University. From 5134 new students, 3785 students (74%) received admissions information through offline media, 1256 students (24%) through online media, and 93 students (2%) through other media. This study aims to determine the effect of the design of visual and verbal web page elements on the buying interest of new students through customer experience. This study combines the PLS-SEM as an analytical method and the Double Diamond method in designing web page recommendations using Warp PLS 7.0 and Adobe XD. 100 experimental respondents were obtained from the purposive sampling method. In the process of designing web page recommendations, the crazy 8 design method will be used to sketch design, supervoting to select design recommendations, and usability testing to test prototypes. The results of this study prove that there is a significant influence between visual web page design on customer experience, verbal web page design on customer experience, verbal web page design on buying interest, customer experience on buying interest, visual web page design on buying interest through customer experience. However, there is also an insignificant effect between visual web page design on buying interest, verbal web page design on buying interest through customer experience. The results of testing the prototype obtained an average score of "Very Good" in its usefulness value.

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.

Intelligent Water Drop Algorithm based Web Page Recommendation Using Linear Regression Learning

Analysis of website visitor for improving the performance of user experience on web portal is primary requirement in this online world. Hence number of web-based feature was used for the study of user behavior and recommend page accordingly. This research work presents the utilization of weblog feature for analysis and pattern learning. Proposed model extract association rule forms the weblog with text pattern format on the web page. Here we have considered the input data set from a live website’s weblogs called as project tunnel.com. Association rule and text URL patterns were used for learning multi-Regression probability generation. Once the regression value is set than Intelligent Water Drop Algorithm will be applied for predicting or recommending the next page as per user previous set of pages, where IWD algorithm increases the accuracy of prediction by utilizing the regression values in fitness function. Live websites weblogs were analyzed for the experiment and comparison of proposed WPPIWD (Web Page Prediction Intelligent Water Drop) has improved the work efficiency when compared to other existing methods.

Personalized recommendation algorithm in social networks based on representation learning

Recommendation algorithm is not only widely used in entertainment media, but also plays an important role in national strategy, such as the recommendation algorithm of byte beating company. This paper studies the personalized recommendation algorithm based on representation learning. The data in social network is complex, and the data mainly exists in various platforms. This paper introduces AI (Artificial Intelligence) algorithm to guide the algorithm of representation learning, and integrates the algorithm steps of representation learning, to realize the implementation of personalized recommendation algorithm in social network, and compares the representation learning algorithm. Finally, this paper designs a method based on heat conduction and text mining to provide users with webpage recommendations and help users better mine interesting popular webpages. Research shows that the performance of IMF is better than that of PMF because it overcomes the sparsity of data by pre-filling. The accuracy of IMF is 3.69% higher than that of PMF on the epinions data set, and 6.24% higher than that of PMF on the double data set. Rtcf, socialmf, tcars, CSIT, isrec, and hesmf have better performance than PMF and IMF. Among them, rtcf, socialmf, tcars, CSIT, isrec, and hesmf improve the MAE performance of PMF by 7.6%, 6.3%, 8.8%, 7.9%, 9.5% and 14.2%, respectively.

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.

Constructing an effective ontology for web page recommendation

Web page recommendation is an approach for suggesting web pages to users based on their past navigational behaviour discovered from their web usage data. The knowledge of the user's behaviour is ex...

Webpage Recommendation for Organization Users via Collaborative Page Weight

Webpage Recommendation for Organization Users via Collaborative Page Weight