A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table

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

A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system

The advancement of technology had encouraged mankind to design and create useful equipment and devices. These equipment enable users to fully utilize them in various applications. Pulp mill is one of the heavy industries that consumes large amount of electricity in its production. Due to this, any malfunction of the equipment might cause mass losses to the company. In particular, the breakdown of the generator would cause other generators to be overloaded. In the meantime, the subsequence loads will be shed until the generators are sufficient to provide the power to other loads. Once the fault had been fixed, the load shedding scheme can be deactivated. Thus, load shedding scheme is the best way in handling such condition. Selected load will be shed under this scheme in order to protect the generators from being damaged. Multi Criteria Decision Making (MCDM) can be applied in determination of the load shedding scheme in the electric power system. In this thesis two methods which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis, a series of analyses are conducted and the results are determined. Among these two methods which are AHP and TOPSIS, the results shown that TOPSIS is the best Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill system. TOPSIS is the most effective solution because of the highest percentage effectiveness of load shedding between these two methods. The results of the AHP and TOPSIS analysis to the pulp mill system are very promising

Self adapting websites: mining user access logs.

The Web can be regarded as a large repository of diversified information in the form of millions of websites distributed across the globe. However, the ever increasing number of websites in the Web has made it extremely difficult for users to find the right informa- tion that satisfies their current needs. In order to address this problem, many researchers explored Web Mining as a way of developing intelligent websites, which could present the information available in a website in a more meaningful way by relating it to a users need. Web Mining applies data mining techniques on web usage, web content or web structure data to discover useful knowledge such as topical relations between documents, users access patterns and website usage statistics. This knowledge is then used to develop intelligent websites that can personalise the content of a website based on a users prefer- ence. However, existing intelligent websites are too focussed on filtering the information available in a website to match a users need, ignoring the true source of users problems in the Web. The majority of problems faced by users in the Web today, can be reduced to issues related to a websites design. All too often, users needs change rapidly but the websites remain static and existing intelligent websites such as customisation, personalisa- tion and recommender systems only provide temporary solutions to this problem. An idea introduced to address this limitation is the development of adaptive websites. Adaptive websites are sites that automatically change their organisation and presentation based on users access patterns. Shortcutting is a sophisticated method used to change the organi- sation of a website. It involves connecting two documents that were previously unlinked in a website by adding a new hyperlink between them based on correlations in users visits. Existing methods tend to minimize the number of clicks required to find a target document by providing a shortcut between the initial and target documents in a users navigational path. This approach assumes the sequence of intermediate documents appearing in the path is insignificant to a users information need and bypasses them. In this work, we explore the idea of adaptive websites and present our approach to it using wayposts to address the above mentioned limitation. Wayposts are intermediate documents in a users path which may contain information significant to a users need that could lead him to his intended target document. Our work identifies such wayposts from frequently travelled users paths and suggests them as potential navigational shortcuts, which could be used to improve a websites organisation

Using Markov Chains for link prediction in adaptive web sites

The large number of Web pages on many Web sites has raised navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users' navigation in the adaptive Web site

Benefits of InterSite Pre-Processing and Clustering Methods in E-Commerce Domain

This paper presents our preprocessing and clustering analysis on the clickstream dataset proposed for the ECMLPKDD 2005 Discovery Challenge. The main contributions of this article are double. First, after presenting the clickstream dataset, we show how we build a rich data warehouse based an advanced preprocesing. We take into account the intersite aspects in the given ecommerce domain, which offers an interesting data structuration. A preliminary statistical analysis based on time period clickstreams is given, emphasing the importance of intersite user visits in such a context. Secondly, we describe our crossed-clustering method which is applied on data generated from our data warehouse. Our preliminary results are interesting and promising illustrating the benefits of our WUM methods, even if more investigations are needed on the same dataset

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research