Digital marketing is constantly adapting and evolving in line with technological advances. One of these advances is the digitalization, which has given rise to Programmatic Advertising (PA). In order to practice PA, the companies need to collect data about consumers' preferences and personal interests. On the other hand, consumers have a need to protect their privacy. The needs of these two parties cross each other which creates the tension, called the Personalization-privacy paradox. In this study, we intend to investigate consumers' attitudes towards PA and whether personalization gives rise to improved browsing experiences or evenviolates their privacy. A quantitative study was conducted, where the independent variable was called Personalization and the dependent variables - Attractiveness, Annoyance, Invasiveness and Trade-off. The results of bivariate regression analysis showed that all of the dependent variables of the study were statistically significant. The results also illustrated that the majority of the respondents experienced PA ads as beneficial, but also invasive. These results are in line with the Utility maximization theory, as PA ads were considered both beneficial and risky. Therefore, consumers would have incentives to disclose their personal information as long as the percieved benefits would outweigh perceived risks, generated by PA. We concluded that there are no clear, predetermined answers to what attitudes consumers have towards PA, but this can vary from case to case, which is in line with both the Privacy calculus theory and the Utility maximization theory. This involves a risk-benefit analysis, performed by consumers, where perceived benefits exceeding perceived risks would generate positive attitudes and vice versa

Purpose: The purpose of this paper is to investigate the impact that personalization as a phenomenon has on consumer purchase intention. Therefore, examining personalization as a phenomenon was performed through three different variables on which personalization has a significant influence. These variables are previous purchase experience, privacy concerns, and attitudes towards personalization.  Method: The primary data collection was developed through a quantitative research approach. Moreover, the survey was used as an instrument to collect the  data from individuals that have made online purchases at least one time in  the past 12 months.  Conclusion: To conclude, our findings show that previous purchase experience  and attitudes towards personalization positively impact consumer purchase intention. On the other hand, our findings show that privacy concerns have no significant correlations with consumer purchase intention and attitudes towards personalization, which means that privacy concerns don't have any impact on purchase intention and attitudes towards personalization.

The tremendous growth of Web has made information overload problem increasingly serious. Users are often confused by huge amount of information available on the internet and they are faced with the problem of finding the most relevant information that meets their needs. Recommender systems have proven to be an important solution approach to this problem. This thesis will present OPENMORE, a movie recommendation system, which is primarily based on content-based filtering technique. The distinctive point of this study lies in the methodology used to construct and update user and item profiles and the optimizations used to fine-tune the constructed user models. The proposed system arranges movie content data as features of a set of dimension slots, where each feature is assigned a stable feature weight regardless of individual movies. These feature weights and the explicit feedbacks provided by the user are then used to construct the user profile, which is fine-tuned through a set of optimization mechanisms. Users are enabled to view their profile, update them and create multiple contexts where they can provide negative and positive feedback for the movies on the feature level.

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An image retrieval system needs to be able to communicate with people using a common language, if it is to serve its user's information need. I propose techniques for interactive image search with the help of visual attributes, which are high-level semantic visual properties of objects (like "shiny" or "natural"), and are understandable by both people and machines. My thesis explores attributes as a novel form of user input for search. I show how to use attributes to provide relevance feedback for image search; how to optimally choose what to seek feedback on; how to ensure that the attribute models learned by a system align with the user's perception of these attributes; how to automatically discover the shades of meaning that users employ when applying an attribute term; and how attributes can help learn object category models. I use attributes to provide a channel on which the user of an image retrieval system can communicate her information need precisely and with as little effort as possible. One-shot retrieval is generally insufficient, so interactive retrieval systems seek feedback from the user on the currently retrieved results, and adapt their relevance ranking function accordingly. In traditional interactive search, users mark some images as "relevant" and others as "irrelevant", but this form of feedback is limited. I propose a novel mode of feedback where a user directly describes how high-level properties of retrieved images should be adjusted in order to more closely match her envisioned target images, using relative attribute feedback statements. For example, when conducting a query on a shopping website, the user might state: "I want shoes like these, but more formal." I demonstrate that relative attribute feedback is more powerful than traditional binary feedback. The images believed to be most relevant need not be most informative for reducing the system's uncertainty, so it might be beneficial to seek feedback on something other than the top-ranked images. I propose to guide the user through a coarse-to-fine search using a relative attribute image representation. At each iteration of feedback, the user provides a visual comparison between the attribute in her envisioned target and a "pivot" exemplar, where a pivot separates all database images into two balanced sets. The system actively determines along which of multiple such attributes the user's comparison should next be requested, based on the expected information gain that would result. The proposed attribute search trees allow us to limit the scan for candidate images on which to seek feedback to just one image per attribute, so it is efficient both for the system and the user. No matter what potentially powerful form of feedback the system offers the user, search efficiency will suffer if there is noise on the communication channel between the user and the system. Therefore, I also study ways to capture the user's true perception of the attribute vocabulary used in the search. In existing work, the underlying assumption is that an image has a single "true" label for each attribute that objective viewers could agree upon. However, multiple objective viewers frequently have slightly different internal models of a visual property. I pose user-specific attribute learning as an adaptation problem in which the system leverages any commonalities in perception to learn a generic prediction function. Then, it uses a small number of user-labeled examples to adapt that model into a user-specific prediction function. To further lighten the labeling load, I introduce two ways to extrapolate beyond the labels explicitly provided by a given user. While users differ in how they use the attribute vocabulary, there exist some commonalities and groupings of users around their attribute interpretations. Automatically discovering and exploiting these groupings can help the system learn more robust personalized models. I propose an approach to discover the latent factors behind how users label images with the presence or absence of a given attribute, from a sparse label matrix. I then show how to cluster users in this latent space to expose the underlying "shades of meaning" of the attribute, and subsequently learn personalized models for these user groups. Discovering the shades of meaning also serves to disambiguate attribute terms and expand a core attribute vocabulary with finer-grained attributes. Finally, I show how attributes can help learn object categories faster. I develop an active learning framework where the computer vision learning system actively solicits annotations from a pool of both object category labels and the objects' shared attributes, depending on which will most reduce total uncertainty for multi-class object predictions in the joint object-attribute model. Knowledge of an attribute's presence in an image can immediately influence many object models, since attributes are by definition shared across subsets of the object categories. The resulting object category models can be used when the user initiates a search via keywords such as "Show me images of cats" and then (optionally) refines that search with the attribute-based interactions I propose. My thesis exploits properties of visual attributes that allow search to be both effective and efficient, in terms of both user time and computation time. Further, I show how the search experience for each individual user can be improved, by modeling how she uses attributes to communicate with the retrieval system. I focus on the modes in which an image retrieval system communicates with its users by integrating the computer vision perspective and the information retrieval perspective to image search, so the techniques I propose are a promising step in closing the semantic gap. / text

During the dot com era the word 'personalisation' was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range/short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user's privacy so that an approach supporting anonymous recommendations through an extended 'Chinese Wall' will be described.

There is a significant number of politicians around the globe who demonstrate the conventions of personal style in their political agenda. Norway’s Prime Minister, Jens Stoltenberg or United States’ President, Barack Obama is a few examples. Personalization of politics was reflected through their Twitter account in the notion of content, pictures, and language tone. In the Indonesian political context, Ridwan Kamil became visible as a prominent leader and received immense popularity on Twitter. Social media platforms have changed the way politicians and citizens interact. They are a platform where individuals and communities share, co-create, discuss, and modify user-generated content. Especially with Twitter, certain features such reply, retweet, and hashtag are powerful tools to integrate their political value and construct their persona on Twitter.  This research is conducted by using mixed-method methodologies. The results from content analysis and discourse analysis will complete each other. The results of the content analysis have shown that what Kamil shares on his Twitter profile is mostly about his philosophy about good governance; social media has become a shortcut in the bureaucracy process and at the same time he manages to seem ordinary and authentic with sharing his personal preferences about music or popular culture phenomenon. Discourse analysis is complementing these findings by showing how Kamil deploys language to produce a certain identity. Kamil is crafting his social media skills and shifting from formal to informal tone at every occasion and construct his persona as professional, fun, and caring. All of these results are important inquiries to describe the politician’s presence on Twitter. As the effect of truth, the Twitter user can still relate to Kamil as an ordinary human being. Focusing on the extent to which the content and users’ personality re-fashion political marketing, the study propose how politician integrate their political value and construct their persona on Twitter. As social media grows globally, the phenomenon of politics and social media also appears in Indonesian political sphere especially Twitter as a new space to offer political rhetoric, posturing, and presentational techniques for political agendas.

Das Smartphone ist mittlerweile ein ständiger Begleiter in unserer Gesellschaft geworden. Die Nutzer fühlen sich jedoch zunehmend belästigt von zu vielen Angeboten, Informationen und Hinweisen, die sie täglich erreichen. Eine Bedarfsanpassung in Form von Personalisierung und Kontextbezug nimmt dementsprechend bei mobilen Diensten eine immer wichtigere Rolle ein. Kontextorientierung bildet heutzutage einen Schwerpunkt im Ubiquitous Computing und geht einher mit der extensiven Nutzung von Sensordaten.

Public education is a cornerstone of our democracy and social advancement. However, current Vermont graduation rates would indicate that public education at the high school level does not address the needs of all twenty-first century learners. Research has revealed that personalization and creating a connected environment are promising innovations for improving education for all students. One structure that supports personalization, high school advisory, provides each student with an adult advisor that knows them well through their high school years. This research on high school advisory in Vermont was divided in two phases: 1) an assessment of the current state of advisory in all Vermont public high schools, and 2) a qualitative study that focused on the perceptions of students, advisors and administrators in 4 Vermont high schools with established advisory programs. In the second phase, a phenomenological framework was used to examine the perceptions of how advisory impacted academics, connectedness and the personalization of the high school experience. Document review, focus forum groups and interviews with the sixteen students, eight advisors and four administrators were conducted over a six-month period. Findings demonstrated that 53 out of 62 high schools in Vermont had some form of advisory program. In the study of four schools, over two-thirds of the students perceived that their high school advisory positively impacted their academic achievement. Advisors and administrators were less clear about the impact, however. Furthermore student-to-student connectedness was described positively by three-quarters of the students. The connection between advisory and personalization of education was the least clear both among students and advisors. All administrators and three-quarters of the advisors felt that in the future, personalization would become an integral part of the advisory program with the advent of Vermont Act 77, the 2013 legislation that mandates personalized learning plans and multiple pathways to graduation. Finally, there was considerable agreement in three schools that a significant roadblock to implementing effective advisories was a lack of support for advisory in the following areas: purpose, time, training and materials.

Cette recherche, réalisée avec l’Agence Nouvelle des Solidarités Actives, a pour objectif d’expliquer la diversité des perspectives d’avenir d’individus confrontés à des situations de précarité et allocataires du Revenu de Solidarité Active. En référence au modèle théorique d’une socialisation active nous formulons l'hypothèse selon laquelle l’impact des dynamiques de précarité sur les projets professionnels des sujets et sur leur attitude affective à l'égard de l'avenir dépend des significations qu'ils accordent aux ressources personnelles (estime de soi) et interpersonnelles (soutiens sociaux perçus et représentations de l’accompagnement RSA) qu’ils mobilisent (ou non) dans les différents temps et milieux de leur vie. Les données ont été recueillies auprès de 172 allocataires du RSA de l’Hérault à l'aide d'un questionnaire. Les résultats amènent à définir cinq types de projets professionnels : « sélectif d’accès à l’emploi stable », de « mobilité professionnelle ascendante », de « préparation de la retraite », d’« accès à l’emploi différé » et d’« accomplissement professionnel ». Ils sont spécifiés par les rapports qu’ils entretiennent avec les projets des sujets dans les autres sphères de leur vie. Les dynamiques de précarité – de « déclassement », d’« exclusion », d’« instabilité » - dans lesquelles se situent les allocataires influent sur le type de projet professionnel qu’ils formulent et sur leur attitude à l'égard de l’avenir. Toutefois, cette influence varie selon les représentations qu’ils ont de leur accompagnement RSA et de leur estime de soi. Dans le prolongement des résultats établis, sont discutées quelques propositions d'évolution de ce dispositif. / This research was conducted in partnership with the Agence Nouvelle des Solidarités Actives. It aims at understanding and explaining the diversity of future perspectives that individuals have when facing situations of precariousness and receiving the French allowance called « Revenu de Solidarité Active » or RSA. Referring to the theoretical model of an active socialization we hypothesize that the impact of precariousness dynamics on individuals' professional projects and on their emotional attitude towards the future depends on the meanings they give to the personal resources (self-esteem) and the interpersonal resources (social support and representations of support from RSA) they mobilize (or not) in different times and domains of their lives. To test this hypothesis, we collected empirical data from 172 individuals benefiting from RSA in the French area of Hérault with a questionnaire. The results led us to define five types of professional projects: "selective access to stable employment”, "upward mobility", "preparation for retirement", "deferred access to employment" and "professional achievement". These professional projects are linked to individuals' projects in their personal, social and family areas. We observed that dynamics of precariousness situations – "downgrading", "exclusion" or "instability" – influence the type of professional project they formulate and their attitude towards the future. However, this influence varies according to their self-esteem and to the support they perceive they get together with RSA. After having established those results, we discussed some proposals to improve the implementation and evolution of RSA.