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

QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach

Mining Frequent Generalized Patterns for Web Personalization in the presence of Taxonomies

The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the Web page level, is not as effective. Moreover, most of the content available online is either explicitly or implicitly characterized by a set of categories organized in a taxonomy, allowing the page-level navigation patterns to be generalized to a higher, aggregate level. In this direction, the authors present the Frequent Generalized Pattern (FGP) algorithm. FGP takes as input the transaction data and a hierarchy of categories and produces generalized association rules that contain transaction items and/or item categories. The results can be used to generate association rules and subsequently recommendations for the users. The algorithm can be applied to the log files of a typical Web site; however, it can be more helpful in a Web 2.0 application, such as a feed aggregator or a digital library mediator, where content is semantically annotated and the taxonomic nature is more complex, requiring us to extend FGP in a version called FGP+. The authors experimentally evaluate both algorithms using Web log data collected from a newspaper Web site

Capturing the voices of children in the Education, Health and Care Plans: are we there yet?

The need for a focus on the voice of children with special educational needs and disabilities (SEND) has received increased recognition internationally both in policy and research. In England, this was emphasized in the new special educational needs framework introduced in 2014. As part of this new policy, children with disabilities and/or additional needs can receive an Education Health and Care (EHC) plan. The EHC plan is a single document that should describe the children's strengths and needs in a multi-disciplinary and holistic way. Section A of the EHC plan must include the child's own perspective. In this context there is much need for evidence on the quality of these new plans and in particular on the quality of the depictions of children's voices. The aim of this study was to address this knowledge gap by analyzing the depictions of children's voices and the process by which these were gathered in 184 EHC plans of children with SEND attending mainstream and special schools in the Greater London area. The content analysis of the section concerning the children's voices was conducted using the categories of a multi-dimensional classification system, which includes aspects relating to the child herself, but also to her environment and relationships—the International Classification of Functioning, Disability and Health (ICF). The findings revealed high levels of variability in the way the voices of children were captured, including the methods used to ascertain their views. Additionally, the type of school that the child was attending seemed to play a significant role on how his/her voice was captured, favoring mainstream schools. The findings of the present study provide the first set of evidence-based data concerning the quality of the content of the newly introduced EHC plans and are discussed in light of the implications they have for policy, practice and further research in the area

‘No Policy is an Island’: Applying International Lessons Learned to Generate Evidence on the Potential of the ICF to Transform Disability Policy and Provision for Children in England

Background: A crucial issue in special educational needs and disability policy and provision is the documentation of children’s functioning, for which many countries have a statutory document. In England this is the education health and care plan. Recent research challenges the quality of these plans. Purpose: To provide evidence on the usefulness of the international classification of functioning, disability and health as a system with potential to support the development of higher quality plans in England. Methods: Twenty-five professionals participated in a one-day training session on the international classification of functioning disability and health, with a focus on designing higher quality SMART targets to be included in children’s plans. Results: Overall, participants regarded the system as useful. Comparison of targets written before and after the training show improvements in relevance, specificity and on the extent to which they were action-oriented and measurable. Conclusions: Results are discussed in light of international lessons learned around the potential of the international classification of functioning disability and health to support policy change. A “no policy is an island” approach is proposed, suggesting local policy-makers should open horizons beyond geographical boundaries in evidence-based decision-making for supporting children with disabilities

The Role of Psychological Sense of School Membership and Postcode as Predictors of Profiles of Socio-Emotional Health in Primary School Children in England

A dual approach to mental health in schools has been widely defended, where the assessment of psychological distress and the examination of strengths/well-being are two separate continua. In line with a well-being approach, school belonging has been referenced as an important indicator of mental health in children. This study explored the predictive role of school sense of belonging alongside other demographic variables (gender, main language spoken at home, and socio-economic status of postcode) on the socio-emotional health profiles of primary school children in England. Children (N = 522) were recruited from three primary schools in Greater London. A survey including measures of school belonging and socio-emotional health was administered to all children. Results showed that it is possible to identify groups of students at primary school level based on socio-emotional health ratings on gratitude, zest, optimism, and perseverance. School sense of membership, as measured by the psychological sense of school membership primary (PSSM-P), was the best predictor of group membership and, together with socio-economic status, explains 37% of the variance in socio-emotional health profiles. Belonging starts affecting well-being and socio-emotional health as early as in primary school, hence the importance of universal screening and early preventive actions to promote well-being in this age range. The study provides evidence supporting the use of the abbreviated (PSSM-P) in predicting socio-emotional health profiles, with potential to complement distress-based measures

Identification of Influential Social Networkers

Online social networking is deeply interleaved in today\u27s lifestyle. People come together and build communities to share thoughts, offer suggestions, exchange information, ideas, and opinions. Moreover, social networks often serve as platforms for information dissemination and product placement or promotion through viral marketing. The success rate in this type of marketing could be increased by targeting specific individuals, called \u27influential users\u27, having the largest possible reach within an online community. In this paper, we present a method aiming at identifying the influential users within an online social networking application. We introduce ProfileRank, a metric that uses popularity and activity characteristics of each user to rank them in terms of their influence. We then assess this algorithm\u27s added value in identifying influential users compared to other commonly used social network analysis metrics, such as the betweenness centrality and the well-known PageRank, by performing an experimental evaluation on a synthetic and a real-life dataset. We also integrate all three metrics in a unified metric and measure its performance