Web Site Personalization based on Link Analysis and Navigational Patterns

The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users’ needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing “next” pages to users based on their current visit and the past users’ navigational patterns. In the vast majority of related algorithms, however, only the usage data are used to produce recommendations, disregarding the structural properties of the web graph. Thus important – in terms of PageRank authority score – pages may be underrated. In this work we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to the web pages based on their importance in the web site’s navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational sub-graphs for online web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches

Improving Web Recommendations Using Web Usage Mining and Web Semantics

This project addresses the topic of improving web recommendations. With the immense increase in the number of websites and web pages on the internet, the issue of suggesting users with the web pages in the area of their interest needs to be addressed as best as possible. Various approaches have been proposed over the years by many researchers and each of them has taken the solution of creating personalized web recommendations a step ahead. Yet, owing to the large possibilities of further improvement, the system proposed in this report takes generating web recommendations one more step ahead. The proposed system uses the information from web usage mining, web semantics and time spent on web pages to improve the recommendations

Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

Rule-based User Characteristics Acquisition from Logs with Semantics for Personalized Web-Based Systems

Personalization of web-based information systems based on specialized user models has become more important in order to preserve the effectiveness of their use as the amount of available content increases. We describe a user modeling approach based on automated acquisition of user behaviour and its successive rule-based evaluation and transformation into an ontological user model. We stress reusability and flexibility by introducing a novel approach to logging, which preserves the semantics of logged events. The successive analysis is driven by specialized rules, which map usage patterns to knowledge about users, stored in an ontology-based user model. We evaluate our approach via a case study using an enhanced faceted browser, which provides personalized navigation support and recommendation

Research Proposal on Distinct Study and Significant of Search Techniques in Web Mining

The goal of this research is to provide a more current evaluation and update of web mining research and how machine learning techniques can be applied to web mining techniques available. Currenttrends in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining.Unlike previous investigators, we divide web mining processes into the following five subtasks such as resource finding and retrieving, information selection and preprocessing, patterns analysis and recognition, validation and interpretation, and visualization.Major limitations of web mining research are lack of suitable test collections that can be reused by researchers and difficulty to collect web usage data across different web sites. Most web mining applications have been reviewed in this research. Although the activities are still in their early stages and should continue to develop as the Web evolves. This research shows that frequent pattern growth algorithm produces more efficient and accurate results to compare with K-Apriori algorithm. The proposed methods were successfully tested and results were observed and compared with existing methods on the web log files using machine learning techniques

Web Mining Functions in an Academic Search Application

This paper deals with Web mining and the different categories of Web mining like content, structure and usage mining. The application of Web mining in an academic search application has been discussed. The paper concludes with open problems related to Web mining. The present work can be a useful input to Web users, Web Administrators in a university environment.Database, HITS, IR, NLP, Web mining

SEMANTICALLY ENRICHED WEB USAGE MINING FOR PREDICTING USER FUTURE MOVEMENTS

ABSTRAC