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

Ranking pages by topology and popularity within web sites

We compare two link analysis ranking methods of web pages in a site. The first, called Site Rank, is an adaptation of PageRank to the granularity of a web site and the second, called Popularity Rank, is based on the frequencies of user clicks on the outlinks in a page that are captured by navigation sessions of users through the web site. We ran experiments on artificially created web sites of different sizes and on two real data sets, employing the relative entropy to compare the distributions of the two ranking methods. For the real data sets we also employ a nonparametric measure, called Spearman's footrule, which we use to compare the top-ten web pages ranked by the two methods. Our main result is that the distributions of the Popularity Rank and Site Rank are surprisingly close to each other, implying that the topology of a web site is very instrumental in guiding users through the site. Thus, in practice, the Site Rank provides a reasonable first order approximation of the aggregate behaviour of users within a web site given by the Popularity Rank

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms

Objective predictors of subjective aesthetic ratings of web pages

This research is concerned with the effect of visual stimulus on decision-­‐making and opinions, what visual aspects of a page affect very early impressions of web sites, and how this relates to computational methods of prediction and evaluation of web pages. The aim of this study was to discover whether there are identifiable visual attributes of web pages that can be used to predict subjective opinions. This was explored through three separate studies. These consisted of two correlational studies and a categorisation task. Participants were gained through convenience and snowball sampling, and the materials reviewed were two distinct sets of web pages. Cards sorts, laddering and an online data collection tool were used to gather the information. Both qualitative and quantitative analysis was used to explore the information. The visual attributes found to correlate with subjective opinions were inconsistent across the two correlational studies. Study One had a number of limitations that may have contributed to this inconsistency. Concrete findings were that levels of encouragement and discouragement influenced by web pages are on two distinct scales, as, although there is a negative correlation between them, a large number of pages were rated poorly on both scales. The similarity between the card sort and questionnaire results had consistent findings for predictors of low-­‐rated web pages. The findings from the cards sorts also show that users are able to make preference judgements of web pages without being able to understand the content. An application of the findings regarding prediction of low-­‐rated pages would be to create web design optimisation system, enabling web pages to be reviewed computationally. Although this should never replace user testing, it may provide an economical alternative during the early stages of design