2,127 research outputs found
Factors Influencing the Quality of the User Experience in Ubiquitous Recommender Systems
The use of mobile devices and the rapid growth of the internet and networking
infrastructure has brought the necessity of using Ubiquitous recommender
systems. However in mobile devices there are different factors that need to be
considered in order to get more useful recommendations and increase the quality
of the user experience. This paper gives an overview of the factors related to
the quality and proposes a new hybrid recommendation model.Comment: The final publication is available at www.springerlink.com
Distributed, Ambient, and Pervasive Interactions Lecture Notes in Computer
Science Volume 8530, 2014, pp 369-37
A review of the role of sensors in mobile context-aware recommendation systems
Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios
GeoNotes: A Location-based Information System for Public Spaces
The basic idea behind location-based information systems is to connect information pieces to positions in outdoor or indoor space. Through position technologies such as Global Positioning System (GPS), GSM positioning, Wireless LAN positioning o
R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems
Mobile Context-Aware Recommender Systems can be naturally modelled as an
exploration/exploitation trade-off (exr/exp) problem, where the system has to
choose between maximizing its expected rewards dealing with its current
knowledge (exploitation) and learning more about the unknown user's preferences
to improve its knowledge (exploration). This problem has been addressed by the
reinforcement learning community but they do not consider the risk level of the
current user's situation, where it may be dangerous to recommend items the user
may not desire in her current situation if the risk level is high. We introduce
in this paper an algorithm named R-UCB that considers the risk level of the
user's situation to adaptively balance between exr and exp. The detailed
analysis of the experimental results reveals several important discoveries in
the exr/exp behaviour
Context-Aware Recommendation Systems in Mobile Environments
Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /
Ambient recommendations in the pop-up shop
In this paper we present the design and first-stage analysis of a purposely built, smart, pop-up wine shop. Our shop learns from visitors’ choices and recommends wine using collaborative filtering and ambient feedback displays integrated into its furniture. Our ambient recommender system was tested in a controlled laboratory environment. We report on the qualitative feedback and between subjects study, testing the influence the system had in wine choice behavior. Participants reported the system helpful, and results from our empirical analysis suggest it influenced buying behavior
Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research
This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing
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