A Web-Based Recommendation System To Predict User Movements Through Web Usage Mining

Web usage mining has become the subject of exhaustive research, as its potential for Web based personalized services, prediction user near future intentions, adaptive Web sites and customer profiling is recognized. Recently, a variety of the recommendation systems to predict user future movements through web usage mining have been proposed. However, the quality of the recommendations in the current systems to predict users‘ future requests can not still satisfy users in the particular web sites. The accuracy of prediction in a recommendation system is a main factor which is measured as quality of the system. The latest contribution in this area achieves about 50% for the accuracy of the recommendations. To provide online prediction effectively, this study has developed a Web based recommendation system to Predict User Movements, named as WebPUM, for online prediction through web usage mining system and proposed a novel approach for classifying user navigation patterns to predict users‘ future intentions. There are two main phases in WebPUM; offline phase and online phase. The approach in the offline phase is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining. In this phase, an undirected graph based on the Web pages as graph vertices and degree of connectivity between web pages as weight of the graph is created by proposing new formula for weight of the each edge in the graph. Moreover, navigation pattern mining has been done by finding connected components in the graph. In the online phase, the longest common subsequence algorithm is used as a new approach in recommendation system for classifying current user activities to predict user next movements. The longest common subsequence is a well-known string matching algorithm that we have utilized to find the most similar pattern between a set of navigation patterns and current user activities for creating the recommendations

WebPUM : a web-based recommendation system to predict user future movements.

Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an action using Web usage mining system and propose a novel approach online prediction for classifying user navigation patterns to predict users’ future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy

A web usage mining approach based on LCS algorithm in online predicting recommendation systems

The Internet is one of the fastest growing areas of intelligence gathering. During their navigation Web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Advanced mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web site. WUM can model user behavior and, therefore, to forecast their future movements. Online prediction is one Web Usage Mining application. However, the accuracy of the prediction and classification in the current architecture of predicting users' future requests systems can not still satisfy users especially in huge Web sites. To provide online prediction efficiently, we advance an architecture for online predicting in Web Usage Mining system and propose a novel approach based on LCS algorithm for classifying user navigation patterns for predicting users' future requests. The Excremental results show that the approach can improve accuracy of classification in the architecture

Leverage web analytics for real time website browsing recommendations

Trabalho apresentado no 5th World Conference on Information Systems and Technologies (WorldCIST’17), 11-13 de abril 2017, Porto Santo, Madeira PortugalAs a websites’ structure grow it is paramount to accommodate the alignment of user needs and experience with the overall websites’ purposes. Toward this requirement, the proposed website navigation recommendation system suggests to users, pages that might be of her interest based on past successful navigation patterns of overall site’s usage. Most of existing recommendation systems adopts traditionally one of the web mining branches. We take a different stance, on web mining usage, and alternatively considered the real time enactment of web analytic tools supported analysis given their current maturity and affordances. On this basis we provide a model, its implementation and evaluation for navigation based recommendations generation and delivery. The developed prototype adopted a SaaS orientation to promote the underlying functionalities integration within any website. Preliminary evaluation’s results seem to favor the validation of the present contribution rational.N/

Database support of Web course development with design patterns

[[abstract]]Current distance learning is mostly based on Web technologies. However, course materials announced as Web documents do not have a normalized structure. It is difficult for students to realize where they are in a Web navigation graph. On the other hand, a text book has a fixed structure, such as the hierarchy of chapters and the index. A text book reader knows how to start searching for information with the common structure of books in his/her mind. If distance learning course materials are organized in one or two patterns, it would be easier for an individual student to follow. We investigate this approach, and propose a system for Web course designs with patterns. The system also serves as a front end module of a Web learning environment which provides automatic assessment of student performance.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20000619~20000621[[iscallforpapers]]Y[[conferencelocation]]Hong Kon

RFID Based Navigation

The RFID (Radio Frequency Identification) based Navigation System is built and designed to allow both commercial and recreational drivers to know and monitor traffic patterns while on the road. By using the RFID based Navigation System, drivers will receive real time data to aid navigation which is a major benefit especially in an emergency situation. Further studies of the RFID technology will potentially open doors to the development of autonomous vehicles. The RFID based Navigation application is available for Android devices, and it presents drivers with real time, current data to aid navigation across a terrain in the shortest possible route. The server/database coordinates information exchange from vehicles to drivers. This is shown on the web based and mobile application. The RFID Reader uses the received RFID tag data to determine specific locations, directions, time, and the speed of the vehicle. Our team went on to divide the system design into three categories: the Android system application, the server/database, and the RFID Reader connection. For the purposes of our project, we used an RC car to test the navigation system. Our team used 50 RFID tags, and we were able to accomplish our goal of presenting navigation data in real time. While our results proved that the system is accurate in real time, covering all roadways will require a large amount of tags leading to time related issues.https://scholarscompass.vcu.edu/capstone/1099/thumbnail.jp

Active Analytics: Adapting Web Pages Automatically Based on Analytics Data

Web designers are expected to perform the difficult task of adapting a site’s design to fit changing usage trends. Web analytics tools give designers a window into website usage patterns, but they must be analyzed and applied to a website\u27s user interface design manually. A framework for marrying live analytics data with user interface design could allow for interfaces that adapt dynamically to usage patterns, with little or no action from the designers. The goal of this research is to create a framework that utilizes web analytics data to automatically update and enhance web user interfaces. In this research, we present a solution for extracting analytics data via web services from Google Analytics and transforming them into reporting data that will inform user interface improvements. Once data are extracted and summarized, we expose the summarized reports via our own web services in a form that can be used by our client side User Interface (UI) framework. This client side framework will dynamically update the content and navigation on the page to reflect the data mined from the web usage reports. The resulting system will react to changing usage patterns of a website and update the user interface accordingly. We evaluated our framework by assigning navigation tasks to users on the UNF website and measuring the time it took them to complete those tasks, one group with our framework enabled, and one group using the original website. We found that the group that used the modified version of the site with our framework enabled was able to navigate the site more quickly and effectively

Personalizing web search and crawling from clickstream data

Our aim is to improve web search engines, approaching the searching problem considering the user, his/her topics of interest and the navigation context. Furthermore, the clickstream also contains patterns inside. Our system will also try to predict the next pages that are going to be visited according to the clickstream. In a personalized search engine, two different users get different results for the same query, because the system considers the interests of each user separately. To personalize search, many sources of information can be used: the bookmarks of the user, his/her geographical location, his navigation history, etc. Web search engines have, broadly speaking, three basic phases. They are crawling, indexing and searching. The information available about the users interest can be considered in some of those three phases, depending on its nature. Work on search personalization already exists. We will see them in Chapter 3. In order to solve the problems of ignorance in relation to the user and his interests, we have developed a system that keeps track of the web pages that the user visits (his clickstream). Our system will analyze the clickstream, and will focus the crawling to pages related to the topics of interest of the user. Furthermore, each time the user executes a query, the system will consider his/her navigation context, and pages related to the navigation context will get better scores. Furthermore, our system also analyzes the clickstream of the user, and retrieves some navigation patterns from it. Those patterns will be used to give some navigation tips to the user based on his navigation context