312 research outputs found
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 userâs 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 /
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
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INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS
The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life
Exploration de la dynamique humaine basée sur des données massives de réseaux sociaux de géolocalisation : analyse et applications
Human dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individualâs behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human culturesLa dynamique humaine est un sujet essentiel de l'informatique centrĂ©e sur lâhomme. Elle se concentre sur la comprĂ©hension des rĂ©gularitĂ©s sous-jacentes, des relations, et des changements dans les comportements humains. En analysant la dynamique humaine, nous pouvons comprendre non seulement des comportements individuels, tels que la prĂ©sence dâune personne Ă un endroit prĂ©cis, mais aussi des comportements collectifs, comme les mouvements sociaux. Lâexploration de la dynamique humaine permet ainsi diverses applications, entre autres celles des services gĂ©o-dĂ©pendants personnalisĂ©s dans des scĂ©narios de ville intelligente. Avec l'omniprĂ©sence des smartphones Ă©quipĂ©s de GPS, les rĂ©seaux sociaux de gĂ©olocalisation ont acquis une popularitĂ© croissante au cours des derniĂšres annĂ©es, ce qui rend les donnĂ©es de comportements des utilisateurs disponibles Ă grande Ă©chelle. Sur les dits rĂ©seaux sociaux de gĂ©olocalisation, les utilisateurs peuvent partager leurs activitĂ©s en temps rĂ©el avec par l'enregistrement de leur prĂ©sence Ă des points d'intĂ©rĂȘt (POIs), tels quâun restaurant. Ces donnĂ©es d'activitĂ© contiennent des informations massives sur la dynamique humaine. Dans cette thĂšse, nous explorons la dynamique humaine basĂ©e sur les donnĂ©es massives des rĂ©seaux sociaux de gĂ©olocalisation. ConcrĂštement, du point de vue individuel, nous Ă©tudions la prĂ©fĂ©rence de l'utilisateur quant aux POIs avec des granularitĂ©s diffĂ©rentes et ses applications, ainsi que la rĂ©gularitĂ© spatio-temporelle des activitĂ©s des utilisateurs. Du point de vue collectif, nous explorons la forme d'activitĂ© collective avec les granularitĂ©s de pays et ville, ainsi quâen corrĂ©lation avec les cultures globale
Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs
Current approaches for points-of-interest (POI) recommendation learn the
preferences of a user via the standard spatial features such as the POI
coordinates, the social network, etc. These models ignore a crucial aspect of
spatial mobility -- every user carries their smartphones wherever they go. In
addition, with growing privacy concerns, users refrain from sharing their exact
geographical coordinates and their social media activity. In this paper, we
present REVAMP, a sequential POI recommendation approach that utilizes the user
activity on smartphone applications (or apps) to identify their mobility
preferences. This work aligns with the recent psychological studies of online
urban users, which show that their spatial mobility behavior is largely
influenced by the activity of their smartphone apps. In addition, our proposal
of coarse-grained smartphone data refers to data logs collected in a
privacy-conscious manner, i.e., consisting only of (a) category of the
smartphone app and (b) category of check-in location. Thus, REVAMP is not privy
to precise geo-coordinates, social networks, or the specific application being
accessed. Buoyed by the efficacy of self-attention models, we learn the POI
preferences of a user using two forms of positional encodings -- absolute and
relative -- with each extracted from the inter-check-in dynamics in the
check-in sequence of a user. Extensive experiments across two large-scale
datasets from China show the predictive prowess of REVAMP and its ability to
predict app- and POI categories.Comment: IEEE Transactions on Big Dat
Towards Proactive Context-aware Computing and Systems
A primary goal of context-aware systems is delivering the right information at the
right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal:
determining what information is relevant, personalizing it based on the usersâ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as âProactive Context-aware Computingâ.
Most of the existing context-aware systems fulfill only a subset of these requirements.
Many of these systems focus only on personalization of the requested information
based on usersâ current context. Moreover, they are often designed for specific domains.
In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate usersâ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains.
To support this dissertation, we explore several directions. Clearly the most significant
sources of information about users today are smartphones. A large amount of usersâ context can be acquired through them and they can be used as an effective means
to deliver information to users. In addition, social media such as Facebook, Flickr and
Foursquare provide a rich and powerful platform to mine usersâ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years.
Since location is one of the most important context for users, we have developed
âLocusâ, an indoor localization, tracking and navigation system for multi-story buildings.
Other important dimensions of usersâ context include the activities that they are engaged
in. To this end, we have developed âSenseMeâ, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the âSenseMeâ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications.
To determine what information would be relevant to usersâ situations, we have developed âTellMeâ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of usersâ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization.
For timely delivery of personalized and relevant information, it is essential to anticipate
and predict usersâ behavior. To this end, we have developed a unified infrastructure,
within the Rover framework, and implemented several novel approaches and algorithms
that employ various contextual features and state of the art machine learning techniques
for building diverse behavioral models of users. Examples of generated models include
classifying usersâ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to
enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing
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