Uniqueness in User Behavior While Using the Web

2016 ◽  
pp. 221-228
Author(s):  
Saniya Zahoor ◽  
Mangesh Bedekar ◽  
Vinod Mane ◽  
Varad Vishwarupe
Keyword(s):  
User Behavior ◽  
The Web

A Survey on Improving the Web Search Ranking by User Behavior Information

2010 ◽  
Author(s):  
Mohamed Husain ◽  
Amarjeet Singh ◽  
Manoj Kumar ◽  
Rakesh Ranjan
Keyword(s):  
Web Search ◽  
User Behavior ◽  
The Web ◽  
Web Search Ranking

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

Web Usage Mining Issues in Big Data

2021 ◽  
pp. 102-112
Author(s):  
Sunny Sharma ◽  
Manisha Malhotra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Data Mining Techniques ◽  
Meaningful Information ◽  
Web Usage ◽  
Use Of Data ◽  
The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

They Know What You Will Do Next Click

2020 ◽  
pp. 100-122
Author(s):  
Serra Çelik
Keyword(s):  
Focus Group ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Log ◽  
Web Usage ◽  
User Behaviors ◽  
Log Files ◽  
The Web

This chapter focuses on predicting web user behaviors. When web users enter a website, every move they make on that website is stored as web log files. Unlike the focus group or questionnaire, the log files reflect real user behavior. It can easily be said that having actual user behavior is a gold value for the organizations. In this chapter, the ways of extracting user patterns (user behavior) from the log files are sought. In this context, the web usage mining process is explained. Some web usage mining techniques are mentioned.

scholarly journals Improving Webpage Access Predictions Based on Sequence Prediction and PageRank Algorithm

10.28945/4176 ◽  
2019 ◽  
Vol 14 ◽  
pp. 027-044 ◽  
Author(s):  
Da Thon Nguyen ◽  
Hanh T Tan ◽  
Duy Hoang Pham
Keyword(s):  
Web Mining ◽  
User Behavior ◽  
User Profile ◽  
Experimental Results ◽  
Prediction Algorithm ◽  
Future Research ◽  
Pagerank Algorithm ◽  
Product Recommendation ◽  
Redundant Data ◽  
The Web

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

scholarly journals Increasing the travel agency’s leading positions by optimizing its website

2020 ◽  
Vol 28 (3) ◽  
pp. 81-91
Author(s):  
Tetyana S. Dronova ◽  
Yana Y. Trygub
Keyword(s):  
Search Engines ◽  
New Technologies ◽  
User Behavior ◽  
Future Research ◽  
Travel Agency ◽  
Research Approach ◽  
Web Resource ◽  
Leading Position ◽  
Google Search ◽  
The Web

Purpose – to study website’s work and content of the travel agency on the example of the "Laspi" travel agency, identify its technical properties and offer methods to increase the web-resource leading position in the Yandex and Google search engines by performing SEO-analysis. Design/Method/Research approach. Internet resources SEO-analysis. Findings.The travel product promotion directly depends on the travel market participants' advertising tools' effectiveness, mainly travel agents. It is determined that one of the new technologies that increase the advertising effectiveness, in particular via the travel agencies’ web resources, is SEO-technology. The authors Identified technical shortcomings of its operation, mainly related to search queries statistics, the subject site visits, the semantic core operation, the site improvement, the site increasing citation, and the number of persistent references in the network. It is proved that updating site development, changing its environment, analyzing user behavior, namely the Og Properties micro markup, updating HTML tags, analytical programs placing, iframe objects selection, and other activities, increase the content uniqueness. As a result, search engines scanned the site, and the search results took first place for the positions essential for the web resource. Originality/Value. The leading positions increasing mechanism application, website operation optimization allow search engines to bring it to the TOP of the most popular travel sites. Theoretical implications. To optimize the web resource operation, a mechanism for improving its leading position is proposed that includes three steps: the general website characteristics of marketing, SEO-analysis, recommendations provision. Practical implications. The research is practical in improving the site’s technical operation and increasing its leading position in Yandex and Google search engines. Research limitations/Future research. Further research aims at the site further analysis after making the proposed changes to its operation. Paper type – empirical.  

Automatic definition of web service call zones based on the classification of context of the mobile device

2021 ◽  
pp. 88-97
Author(s):  
Михаил Леонтьевич Воскобойников ◽  
Роман Константинович Федоров ◽  
Геннадий Михайлович Ружников
Keyword(s):  
Web Services ◽  
Web Service ◽  
Mobile Device ◽  
User Behavior ◽  
Application Programming Interface ◽  
Service Invocation ◽  
Indoor Geolocation ◽  
Iot Devices ◽  
The Web

Предложен метод автоматизации активации устройств Интернета вещей на основе классификации геопозиции мобильного устройства. В отличие от других методов пользователь обучает систему активации устройств с помощью примеров и контрпримеров, что значительно снижает требования к квалификации пользователя. Проведено тестирование метода на таких двух устройствах, как шлагбаум и электромеханический замок двери. Полученные результаты тестирования позволяют судить о работоспособности метода и возможности его использования в системах умного дома и города. Most IoT devices provide an application programming interface such as web service that allows controlling these IoT devices over Internet using a mobile phone. Activation of IoT devices is performed according to the status of user behavior. Both user behavior and activation of IoT devices are periodical. An activation of IoT device is often related with a user geolocation which can be defined by sensors of the mobile device. A method for automated activation of IoT devices based on classification of geolocation of mobile device is proposed. The method implements a supervised learning that simplifies automate activation of IoT devices for the end users. Existing methods demand appropriate end user qualification and require long time to automate activation. For indoor geolocation of the mobile device information from Wi-Fi access points and geolocation GPS sensor is utilized. Data of Wi-Fi and GPS sensors is used to form context of a mobile device. Based on context examples of invoking/not invoking web services the spatial areas are formed. When the mobile device context is within the web service invocation area, the web service is invoked and the associated IoT device is activated. To implement the method, an Android application was developed. The method was tested on a training set that contained 100 training examples of calling two web services: opening an electromechanical door lock and opening a barrier. As a result of testing, the accuracy of classifying the context of a mobile device was 98 percent. The results obtained can be used in the development of smart home and smart city systems.

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