Theoretical model of user acceptance : in the view of measuring success in web personalization

This paper attempts to develop a theoretical acceptance model for measuring Web personalization success. Key factors impacting Web personalization acceptance are identified from a detailed literature review. The final model is then cast in a structural equation modeling (SEM) framework comprising nineteen manifest variables, which are grouped into three focal behaviors of Web users. These variables could provide a framework for better understanding of numerous factors that contribute to the success measures of Web personalization technology. Especially, those concerning the quality of personalized features and how personalized information through personalized Website can be delivered to the user. The interrelationship between success constructs is also explained. Empirical validations of this theoretical model are expected on future research.

User behaviour modelling in a multi-dimensional environment for personalization and recommendation

Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.

Personalization in tag ontology learning for recommendation making

Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users’ information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation.

New genetic markers for treatment personalization in pediatric acute lymphoblastic leukemia

La Leucemia Linfoblástica Aguda (LLA) es el cáncer pediátrico más común. Es un desorden de las células linfoblásticas, que son las precursoras de las células linfáticas, y se caracteriza por la acumulación en médula ósea y sangre de pequeñas células blásticas con poco citoplasma y cromatina dispersa. En las últimas décadas, se ha conseguido aumentar la supervivencia del 10% al 80% pero todavía hay un 20% de pacientes que no responden al tratamiento. Esta mejoría se ha conseguido mediante la implantación de terapias combinadas y la adecuación de la terapia a grupos de riesgo. Los pacientes se separan en tres grupos de riesgo, Riesgo Estándar (RE), Alto Riesgo (AR) y Muy Alto Riesgo (MAR), en base a marcadores pronósticos, entre los que se incluyen alteraciones citogenéticas. Sin embargo, a lo largo del tratamiento, nos encontramos con dos problemas:1) Por un lado, algunos de los pacientes incluidos en el grupo de RE y AR no responden bien al tratamiento y pasan AR y MAR respectivamente. Esto quiere decir que los grupos de riesgo no están bien definidos. Por lo tanto, sería de interés poder caracterizar los pacientes que realmente son RE y AR y aquéllos que desde un principio deberían haber sido considerados como de mayor riesgo.2) Por otro lado, un alto porcentaje de pacientes experimenta toxicidad, que puede llegar a ser muy grave en algunos casos, siendo necesario parar el tratamiento. Por este motivo, sería altamente beneficioso poder reconocer a los pacientes que van a ser más sensibles al tratamiento para, de ese modo, poder ajustar la dosis.Por todo esto, creemos que una mejor asignación de los pacientes de LLA a grupos de riesgo y la personalización de la dosis, mediante nuevos marcadores genéticos, permitiría mejorar la respuesta al tratamiento.En este estudio nos planteamos, por lo tanto, dos objetivos: 1) Llevar a cabo la identificación de nuevas alteraciones genéticas presentes en el tumor para una mejor caracterización del riesgo y 2) Realizar una caracterización genética del individuo que permita predecir la respuesta al tratamiento.

Open Personalization: Involving Third Parties in Improving the User Experience of Websites

Traditional software development captures the user needs during the requirement analysis. The Web makes this endeavour even harder due to the difficulty to determine who these users are. In an attempt to tackle the heterogeneity of the user base, Web Personalization techniques are proposed to guide the users’ experience. In addition, Open Innovation allows organisations to look beyond their internal resources to develop new products or improve existing processes. This thesis sits in between by introducing Open Personalization as a means to incorporate actors other than webmasters in the personalization of web applications. The aim is to provide the technological basis that builds up a trusty environment for webmasters and companion actors to collaborate, i.e. "an architecture of participation". Such architecture very much depends on these actors’ profile. This work tackles three profiles (i.e. software partners, hobby programmers and end users), and proposes three "architectures of participation" tuned for each profile. Each architecture rests on different technologies: a .NET annotation library based on Inversion of Control for software partners, a Modding Interface in JavaScript for hobby programmers, and finally, a domain specific language for end-users. Proof-of-concept implementations are available for the three cases while a quantitative evaluation is conducted for the domain specific language.

Multi-agent system for personalization of location-based services

The current ubiquitous network access and increase in network bandwidth are driving the sales of mobile location-aware user devices and, consequently, the development of context-aware applications, namely location-based services. The goal of this project is to provide consumers of location-based services with a richer end-user experience by means of service composition, personalization, device adaptation and continuity of service. Our approach relies on a multi-agent system composed of proxy agents that act as mediators and providers of personalization meta-services, device adaptation and continuity of service for consumers of pre-existing location-based services. These proxy agents, which have Web services interfaces to ensure a high level of interoperability, perform service composition and take in consideration the preferences of the users, the limitations of the user devices, making the usage of different types of devices seamless for the end-user. To validate and evaluate the performance of this approach, use cases were defined, tests were conducted and results gathered which demonstrated that the initial goals were successfully fulfilled.

An enhanced content selection mechanism for personalization of video news programmes

In this paper, we propose a content selection framework that improves the users` experience when they are enriching or authoring pieces of news. This framework combines a variety of techniques to retrieve semantically related videos, based on a set of criteria which are specified automatically depending on the media`s constraints. The combination of different content selection mechanisms can improve the quality of the retrieved scenes, because each technique`s limitations are minimized by other techniques` strengths. We present an evaluation based on a number of experiments, which show that the retrieved results are better when all criteria are used at time.

Supporting knowledge strategy in consulting organizations : codification, personalization or both

This paper reports on results from an ongoing study of the information infrastructure support for organizational knowledge strategy. We assess the applicability and predictions of the widely cited model of Hansen, Nohria and Tierney’s on knowledge strategy and infrastructure support. We do so by means of two case studies that we conducted in the consulting sector. Our findings indicate support for the Hansen et al.’s model as each of the organizations has predominately pursued a knowledge strategy consistent with the model’s predictions. However, we also find that in so doing, organizations can incur significant opportunity costs if either a codification or a personalization knowledge strategy is allowed to predominate to such an extent that it crowds-out the alternative knowledge strategy.

Campaign strategies at the Swiss National Elections 2015: Nationalization, Coordination, and Personalization

This research note examines parties’ campaign strategies in the 2015 Swiss elections. We base our analyses on a collection of more than 5000 party advertisements, which were published in the forefront of the national elections in more than 50 daily and weekly national and cantonal print media. By comparing the amount of party and candidate ads, as well as the content and nature of the political advertisements, we explore the degree of professionalization of electoral campaigns in the most recent federal elections in terms of nationalization, coordination and personalization. First results show that although national campaign coordination exists, Swiss elections are to a considerable extent still cantonal and personal affairs.

Analyzing Visitor Perceptions of Personalization in Art Museum Interactive Technology

Thesis (Master's)--University of Washington, 2016-06

Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.

The grip of personalization in adult social care:between managerial domination and fantasy

This paper examines the 'ideological grip' of personalization. It does so empirically, tracking the trajectory of personalization through austerity budgeting in one English local authority. In this case, personalization continued to signify hope and liberation even though the most draconian cuts in the Council's history effectively rendered personalization a practical impossibility. This requires critical theorization. Two bodies of theory are interrogated. First Boltanski's sociology of critique, and, in particular, his notion of managerial domination illuminate the way in which change imperatives and crises come to cement ideological formations. Here it is argued that the articulation of personalization with transformation lends itself to managerial domination. It is further argued, though, that while institutional actors may be able to manipulate the symbolic to evade, what Boltanski terms, deconstructionist critique, this cannot entirely explain the hold of this particular discourse. Here, the Lacanian concept of enjoyment is deployed to interrogate its extra-symbolic function and fantasmatic form. Finally, the paper explores the political implications of such affective attachment and, in particular, the guarantee that personalization offers in a period of welfare state decline. © The Author(s) 2012.