The Future of Personalisation at News Websites: Lessons from a Longitudinal Study

This paper tracks the recent history of personalization at national news websites in the United Kingdom and United States, allowing an analysis to be made of the reasons for and implications of the adoption of this form of adaptive interactivity. Using three content surveys conducted over three and a half years, the study records—at an unprecedented level of detail—the range of personalization features offered by contemporary news websites, and demonstrates how news organizations increasingly rely on software algorithms to predict readers’ content preferences. The results also detail how news organizations’ deployment of personalization on mobile devices, and in conjunction with social networking platforms, is still at an early stage. In addressing the under-researched but important—and increasingly prevalent—phenomenon of personalization, this paper contributes to debates on journalism’s future funding, transparency, and societal benefits

Making 'The Daily Me': Technology, economics and habit in the mainstream assimilation of personalized news

The mechanisms of personalization deployed by news websites are resulting in an increasing number of editorial decisions being taken by computer algorithms — many of which are under the control of external companies — and by end users. Despite its prevalence, personalization has yet to be addressed fully by the journalism studies literature. This study defines personalization as a distinct form of interactivity and classifies its explicit and implicit forms. Using this taxonomy, it surveys the use of personalization at 11 national news websites in the UK and USA. Research interviews bring a qualitative dimension to the analysis, acknowledging the influence that institutional contexts and journalists’ attitudes have on the adoption of technology. The study shows how: personalization informs debates on news consumption, content diversity, and the economic context for journalism; and how it challenges the continuing relevance of established theories of journalistic gate-keeping

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster. © 2005, Australian Computer Society, Inc

An MDA approach for goal-oriented requirement analysis in Web engineering

Web designers usually ignore how to model real user expectations and goals, mainly due to the large and heterogeneous audience of the Web. This fact leads to websites which are difficult to comprehend by visitors and complex to maintain by designers. In order to ameliorate this scenario, an approach for using the i* modeling framework in Web engineering has been developed in this paper. Furthermore, due to the fact that most of the existing Web engineering approaches do not consider how to derive conceptual models of the Web application from requirements analysis we also propose the use of MDA (Model Driven Architecture) in Web engineering for: (i) the definition of the requirements of a Web application in a Computational Independent Model (CIM), (ii) the description of Platform Independent Models (PIMs), and (iii) the definition of a set of QVT (Query/View/Transformation) transformations for the derivation of PIMs from requirements specification (CIM), thus to enable the automatic generation of Web applications. Finally, we include a sample of our approach in order to show its applicability and we describe a prototype tool as a proof of concept of our research.This work has been partially supported by the MANTRA project (GRE09-17) from the University of Alicante, and by the MESOLAP (TIN2010-14860) from the Spanish Ministry of Education and Science. José Alfonso Aguilar is subventioned by CONACYT (Consejo Nacional de Ciencia y Tecnología) Mexico and University of Sinaloa, Mexico

Web Usage Mining and User Behaviour Prediction

Today, Internet is playing such a significant role in our day-to-day life. We have witnessed the evermore- interesting and upcoming publishing medium is the World Wide Web (WWW). The rapid growth in the volume of information available over the WWW and number of its� potential users� has leads to difficulties in providing effective search service for users�, resulting in decrease in the web performance. Web Usage Mining is an area, where the navigational access behaviour of users� over the web is tracked and analyzed. So that websites owner can easily identify the access patterns of its users�. By collecting and analyzing this behaviour of user activities, websites owner can enhance the quality and performance of services to catch the attention of existing as well as new customers. This research paper intends to provide an overview of past and current evaluation in users� future request prediction using Web Usage Mining

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