Pervasive computing environment and users’ demand for multimedia personalization precipitate a need for personalization tools to help people access desired multimedia content at anytime, anywhere, through any devices. User preference learning plays an important role in multimedia personalization. In this paper, we propose a learning approach to acquire and update user preference for multimedia ...

Modern recommender systems rely on user preference data to understand, analyze and provide items of interest to users. However, for some domains, collecting and sharing such data can be problematic: it may be expensive to gather data from several users, or it may be undesirable to share real user data for privacy reasons. We therefore propose a new model for generating realistic preference data...

The main purpose of this study is to investigate the preferences of urban dwellers for variouselements of urban parks in order to provide some important suggestions for rehabilitation of urban deficient parks. Inthis regard, this study has conducted a survey in one of the oldest parks in Khorramabad, Iran, to reveal the overallsatisfaction of the park situation and to explore the preferences of...

Bayesian approaches to preference learning using Gaussian Processes (GPs) are attractive due to their ability to explicitly model uncertainty in users’ latent utility functions; unfortunately existing techniques have cubic time complexity in the number of users, which renders this approach intractable for collaborative preference learning over a large user base. Exploiting the observation that ...

OBJECTIVE Three experiments examined the effects of various feedback displays on user preference, apparent waiting durations, waiting time reasonableness, and other user experience measures. BACKGROUND User interface guidelines advocate keeping users informed about system status; however, the duration estimation literature shows that focusing on temporal information makes the wait seem longer...

The phenomena of illegal disclosure of user privacy has become more serious considerably in the last years. How to protect user privacy and prevent user privacy from illegal disclosure has become of great interest to researchers. In this paper, we propose an approach of user privacy protection based on ontology inference. We create privacy ontology through detailed analysis of privacy domain. T...

Traditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions/ratings are highly scarce. To address this, we propose a learning framework that improves with synthetic feedback loop (CF-SFL) simulate user feedback. The proposed consists of and virtual user. is formulated as CF model, recommending items according observed preference. est...

Most of prior works optimize caching policies based on the following assumptions: 1) every user initiates request according to content popularity, 2) all users are with the same activity level, and 3) users are uniformly located in the considered region. In practice, these assumptions are often not true. In this paper, we explore the benefit of optimizing caching policies for base stations by e...

Recommendations play an important role in Web-based commerce. Some advertisement agencies are now trying to push personalized recommendations to mobile phones. As mobile users almost always carry their mobile phones, it is important to recommend content that is related to the user’s real world activity in order to improve the quality of the recommendations. This paper realizes highly effective ...

Preference elicitation is concerned with inferring a latent utility function from user feedback. Typical elicitation approaches iteratively query the user about (supposedly informative) pairs of candidate configurations; the collected pairwise preference constraints are used to improve the utility estimate. The ongoing transition from flat to structured configuration spaces is opening promising...