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

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

Website Personalization Based on Demographic Data

This study focuses on websites personalization based on user's demographic data. The main demographic data that used in this study are age, gender, race and occupation. These data is obtained through user profiling technique conducted during the study. Analysis of the data gathered is done to find the relationship between the user's demographic data and their preferences for a website design. These data will be used as a guideline in order to develop a website that will fulfill the visitor's need. The topic chose was Obesity. HCI issues are considered as one of the important factors in this study which are effectiveness and satisfaction. The methodologies used are website personalization process, incremental model, combination of these two methods and Cascading Style Sheet (CSS) which discussed detail in Chapter 3. After that, we will be discussing the effectiveness and evaluation of the personalization website that have been built. Last but not least, there will be conclusion that present the result of evaluation of the websites made by the respondents

Easy Creation of Semantics-Enhanced Digital Artwork Collections

In this paper we propose an approach for cost-effective employing of semantic technologies to improve the efficiency of searching and browsing of digital artwork collections. It is based on a semi-automatic creation of a Topic Map-based virtual art gallery portal by using existing Topic Maps tools. Such a ‘cheap’ solution could enable small art museums or art-related educational programs that lack sufficient funding for software development and publication infrastructure to take advantage of the emerging semantic technologies. The proposed approach has been used for creating the WSSU Diggs Gallery Portal

Is adaptation of e-advertising the way forward?

E-advertising is a multi-billion dollar industry that has shown exponential growth in the last few years. However, although the number of users accessing the Internet increases, users don’t respond positively to adverts. Adaptive e-advertising may be the key to ensuring effectiveness of the ads reaching their target. Moreover, social networks are good sources of user information and can be used to extract user behaviour and characteristics for presentation of personalized advertising. Here we present a two-sided study based on two questionnaires, one directed to Internet users and the other to businesses. Our study shows that businesses agree that personalized advertising is the best way for the future, to maximize effectiveness and profit. In addition, our results indicate that most Internet users would prefer adaptive advertisements. From this study, we can propose a new design for a system that meets both Internet users’ and businesses’ requirements

Analysis of Web Log from Database System utilizing E-web Miner Algorithm

Enormous content of information on the World Wide Web makes it clear for contender for data mining research. Data Mining Technique application is used to the World Wide Web referred as Web mining where this term has been used diverse ways. Web Log Mining is one of the Web based application where it confronts with large amount of log information. In order to produce the web log through portal usage patterns and user behaviors’ recognition, this intended work is an endeavor to apply an efficient web mining algorithm for web log analysis, which is applied to identify the context related with the web design of an e- business web portal that requests security. Because of tremendous utilization of web, web log documents have a tendency to become large resulting in noisy data files. It can find the browsing patterns of client and some class of relationships between the web pages. Here we analyze the logs using web mining algorithm. Whatever the result we will get compare within the Apriori, AprioriAll and E-Web Miner Algorithm. Through the analysis we recognize that web mining algorithm called E-web miner i.e. Efficient Web Mining performs better considering space and time complexity. It can likewise be verified by comparison, candidate sets are much smaller and our results show number of database scanning get reduced due to implementation of E-Web Miner Algorithm. DOI: 10.17762/ijritcc2321-8169.15077

REVIEW PAPER ON WEB PAGE PREDICTION USING DATA MINING

The continuous growth of the World Wide Web imposes the need of new methods of design and determines how to access a web page in the web usage mining by performing preprocessing of the data in a web page and development of on-line information services. The need for predicting the user’s needs in order to improve the usability and user retention of a web site is more than evident now a day. Without proper guidance, a visitor often wanders aimlessly without visiting important pages, loses interest, and leaves the site sooner than expected. In proposed system focus on investigating efficient and effective sequential access pattern mining techniques for web usage data. The mined patterns are then used for matching and generating web links for online recommendations. A web page of interest application will be developed for evaluating the quality and effectiveness of the discovered knowledge.   Keyword: Webpage Prediction, Web Mining, MRF, ANN, KNN, GA