Current approaches to privacy policy comparison use strict evaluation criteria (e.g. user preferences) and are unable to state how close a given policy is to fulfil these criteria. More flexible approaches for policy comparison is a prerequisite for a number of more advanced privacy services, e.g. improved privacy-enhanced search engines and automatic learning of privacy preferences. This paper...

Previous research showed that choice-based preference elicitation can be successfully used to reduce effort during user cold start, resulting in an improved user satisfaction with the recommender system. However, it has also been shown to result in highly popular recommendations. In the present study we investigate if trailers reduce this bias to popular recommendations by informing the user an...

In previous research we have shown the architecture and application of a case-based reasoning (CBR) system used to discover user preferences in an existing mixed-initiative dialogue system. In this paper we apply this CBR system to increasingly large datasets to test its ability to maintain nearreal time performance in generating new user preferences. We also propose possible future application...

In the contemporary complex world decisions are made by an imperfect participant devoting limited deliberation resources to any decision-making task. A normative decision-making (DM) theory should provide support systems allowing such a participant to make rational decisions in spite of the limited resources. Efficiency of the support systems depends on the interfaces enabling a participant to ...

Systems supporting decision making became almost inevitable in the modern complex world. Their efficiency depends on the sophisticated interfaces enabling a user take advantage of the support while respecting the increasing on-line information and incomplete, dynamically changing user’s preferences. The best decision making support is useless without the proper preference elicitation. The paper...

This paper introduces a novel approach for pairwise preference learning through a combination of an evolutionary method and random forest. Grammatical evolution is used to describe the structure of the trees in the Random Forest (RF) and to handle the process of evolution. Evolved random forests are evaluated based on their efficiency in predicting reported preferences. The combination of these...

CP-networks have been proposed as a simple and intuitive graphical tool for representing conditional ceteris paribus preference statements over the values of a set of variables. While the problem of reasoning with CP-networks has been receiving some attention, there are very few works that address the problem of learning CP-networks. In this work we investigate the task of learning CPnetworks, ...

Discovering user preference is an important task in various database applications, such as searching product information and rating goods and services. Previous work addressing user preference in databases either assumes that users are able to formulate explicit preference criteria when querying, which may not be feasible in reality, or develops mining techniques to discover users’ implicit pre...

Recommender systems play an important role in supporting people getting items they like. One type of recommender systems is userbased collaborative filtering. The fundamental assumption of user-based collaborative filtering is that people who share similar preferences for common items behave similar in the future. The similarity of user preferences is computed globally on common rated items suc...