2,156 research outputs found
IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS
Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social
attributes information of destinations is made a factor in the destination recommendation process
Personalization in cultural heritage: the road travelled and the one ahead
Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge
technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user
(e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed
Improving the Dependability of Destination Recommendations using Information on Social Aspects
Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process.Content-based filtering; Recommender Systems; Ontology; Social Attributes, Destination recommendation
Development of an ontology for the inclusion of app users with visual impairments
Approximately 15% of the world’s population have some form of disability and the majority
use apps on their mobile devices to help them in their daily lives with communication, healthcare, or for
entertainment purposes. It is not, however, easy for users with impairments to choose the most suitable apps
since this will depend on their particular personal characteristics or circumstances in a specific context, and
because such users require apps with certain accessibility features which are not always specified in the app
description. In order to overcome such difficulties, it is necessary to obtain a user profile that gathers the
user’s personal details, abilities, disabilities, skills, and interests to facilitate selection. The basis for our
research work is to develop an app that recommends a set of apps to users with disabilities. In this respect,
the focus of this paper is to obtain a semantic user profile model on which more precise search requests can
be performed. The disability we have chosen to concentrate on is that of visual impairment. We propose an
ontology-based user profile that matches users’ characteristics, disabilities, and interests, and which not
only simplifies the classification process but also provides a mechanism for linking them with existing
disability ontologies, assistive devices, accessibility concepts, etc. Moreover, thanks to the inclusion of
semantic relations and rules, it is possible to reason and infer new information that can be used to make
more personalized recommendations than a simple app store search.Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion)
PID2019-109644RB-I00/AEI/10.13039/50110001103
Anticipating Information Needs Based on Check-in Activity
In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM '17), 201
Quality of experience in affective pervasive environments
The confluence of miniaturised powerful devices, widespread communication networks and mass remote storage has caused a fundamental shift in the user interaction design paradigm. The distinction between system and user in pervasive environments is evolving into an increasingly integrated loop of interaction, raising a number of opportunities to provide enhanced and personalised experiences.
We propose a platform, based on a smart architecture, to address the identified opportunities in pervasive computing. Smart systems aim at acting upon an environment for improving quality of experience: a subjective measure that has been defined as an emotional reaction to products or services. The inclusion of an emotional dimension allows us to measure individual user responses and deliver personalised services with the potential to influence experiences positively.
The platform, Cloud2Bubble, leverages pervasive systems to aggregate user and environment data with the goal of addressing personal preferences and supra-functional requirements. This, combined with its societal implications, results in a set of design principles as a concrete fruition of design contractualism.
In particular, this thesis describes:
- a review of intelligent ubiquitous environments and relevant technologies, including a definition of user experience as a dynamic affective construct;
- a specification of main components for personal data aggregation and service personalisation, without compromising privacy, security or usability;
- the implementation of a software platform and a methodological procedure for its instantiation;
- an evaluation of the developed platform and its benefits for urban mobility and public transport information systems;
- a set of design principles for the design of ubiquitous systems, with an impact on individual experience and collective awareness.
Cloud2Bubble contributes towards the development of affective intelligent ubiquitous systems with the potential to enhance user experience in pervasive environments. In addition, the platform aims at minimising the risk of user digital exposure while supporting collective action.Open Acces
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