Design Principals of Social Navigation

8th Delos Workshop on "User Interfaces for Digital Libraries" (on 21 October it will be held in conjuction with the 4th ERCIM Workshop on "User Interfaces for All"), SICS, Kista, Sweden, 21-23 October 1998PERSON

Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

End-User Development for Artificial Intelligence: A Systematic Literature Review

In recent years, Artificial Intelligence has become more and more relevant in our society. Creating AI systems is almost always the prerogative of IT and AI experts. However, users may need to create intelligent solutions tailored to their specific needs. In this way, AI systems can be enhanced if new approaches are devised to allow non-technical users to be directly involved in the definition and personalization of AI technologies. End-User Development (EUD) can provide a solution to these problems, allowing people to create, customize, or adapt AI-based systems to their own needs. This paper presents a systematic literature review that aims to shed the light on the current landscape of EUD for AI systems, i.e., how users, even without skills in AI and/or programming, can customize the AI behavior to their needs. This study also discusses the current challenges of EUD for AI, the potential benefits, and the future implications of integrating EUD into the overall AI development process.Comment: This version did not undergo peer-review. A corrected version is published by Springer Nature in the Proceedings of 9th International Syposium on End-User Development (ISEUD 2023). DOI: https://doi.org/10.1007/978-3-031-34433-6_

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Personalized Emphasis Framing for Persuasive Message Generation

In this paper, we present a study on personalized emphasis framing which can be used to tailor the content of a message to enhance its appeal to different individuals. With this framework, we directly model content selection decisions based on a set of psychologically-motivated domain-independent personal traits including personality (e.g., extraversion and conscientiousness) and basic human values (e.g., self-transcendence and hedonism). We also demonstrate how the analysis results can be used in automated personalized content selection for persuasive message generation

Credibility of Health Information and Digital Media: New Perspectives and Implications for Youth

Part of the Volume on Digital Media, Youth, and Credibility. This chapter considers the role of Web technologies on the availability and consumption of health information. It argues that young people are largely unfamiliar with trusted health sources online, making credibility particularly germane when considering this type of information. The author suggests that networked digital media allow for humans and technologies act as "apomediaries" that can be used to steer consumers to high quality health information, thereby empowering health information seekers of all ages

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Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given