15,208 research outputs found
Towards a Two-Dimensional Framework for User Models
The focus if this paper is user modeling in the context of personalization of information systems. Such a personalization is essential to give users the feeling that the system is easily accessible. The way this adaptive personalization works is very dependent on the adaptation model that is chosen.
We introduce a generic two-dimensional classification framework for user modeling systems. This enables us to clarify existing as well as new applications in the area of user modeling. In order to illustrate our framework we evaluate push and pull based user modeling
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
Does your profile say it all? Using demographics to predict expressive head movement during gameplay
In this work, we explore the relation between expressive head movement and user profile information in game play settings. Facial gesture analysis cues are statistically correlated with players' demographic characteristics in two different settings, during game-play and at events of special interest (when the player loses during game play). Experiments were conducted on the Siren database, which consists of 58 participants, playing a modified version of the Super Mario. Here, as player demographics are considered the gender and age, while the statistical importance of certain facial cues (other than typical/universal facial expressions) was analyzed. The proposed analysis aims at exploring the option of utilizing demographic characteristics as part of users' profiling scheme and interpreting visual behavior in a manner that takes into account those features.peer-reviewe
Automated user modeling for personalized digital libraries
Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to
improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in
an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy userâs necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information
Theory-based user modeling for personalized interactive information retrieval
In an effort to improve usersâ search experiences during their information seeking process, providing a personalized information retrieval system is proposed to be one of the effective approaches. To personalize the search systems requires a good understanding of the users. User modeling has been approved to be a good method for learning and representing users. Therefore many user modeling studies have been carried out and some user models have been developed. The majority of the user modeling studies applies inductive approach, and only small number of studies employs deductive approach. In this paper, an EISE (Extended Information goal, Search strategy and Evaluation threshold) user model is proposed, which uses the deductive approach based on psychology theories and an existing user model. Ten usersâ interactive search log obtained from the real search engine is applied to validate the proposed user model. The preliminary validation results show that the EISE model can be applied to identify different types of users. The search preferences of the different user types can be applied to inform interactive search system design and development
Towards a kansei-based user modeling methodology for eco-design
We propose here to highlight the benefits of building a framework linking Kansei Design (KD), User Centered Design (UCD) and Eco-design, as the correlation between these fields is barely explored in research at the current time. Therefore, we believe Kansei Design could serve the goal of achieving more sustainable products by setting up an accurate understanding of the user in terms of ecological awareness, and consequently enhancing performance in the Eco-design process. In the same way, we will consider the means-end chain approach inspired from marketing research, as it is useful for identifying ecological values, mapping associated functions and defining suitable design solutions. Information gathered will serve as entry data for conducting scenario-based design, and supporting the development of an Eco-friendly User Centered Design methodology (EcoUCD).ANR-ECOUS
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