2,191 research outputs found
Multi-Dimensional-Personalization in mobile contexts
During the dot com era the word "personalisationâ was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate.
Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services â with the consequence that the right information or service could therefore be offered at the right place, at the right time.
The growing availability of "Location-based Servicesâ for mobile phones is a new target for the use of personalisation. "Location-based Servicesâ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place.
This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the userâs privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wallâ will be described
Recommender systems in industrial contexts
This thesis consists of four parts: - An analysis of the core functions and
the prerequisites for recommender systems in an industrial context: we identify
four core functions for recommendation systems: Help do Decide, Help to
Compare, Help to Explore, Help to Discover. The implementation of these
functions has implications for the choices at the heart of algorithmic
recommender systems. - A state of the art, which deals with the main techniques
used in automated recommendation system: the two most commonly used algorithmic
methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization
methods are detailed. The state of the art presents also purely content-based
methods, hybridization techniques, and the classical performance metrics used
to evaluate the recommender systems. This state of the art then gives an
overview of several systems, both from academia and industry (Amazon, Google
...). - An analysis of the performances and implications of a recommendation
system developed during this thesis: this system, Reperio, is a hybrid
recommender engine using KNN methods. We study the performance of the KNN
methods, including the impact of similarity functions used. Then we study the
performance of the KNN method in critical uses cases in cold start situation. -
A methodology for analyzing the performance of recommender systems in
industrial context: this methodology assesses the added value of algorithmic
strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201
A context aware recommender system for tourism with ambient intelligence
Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on userâs personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO
O sistema de recomendação (RS) detĂ©m um lugar significativo na ĂĄrea do sector do turismo. O principal fator do planeamento de viagens Ă© selecionar pontos de interesse relevantes (PoI) do domĂnio do turismo. O sistema de recomendação (SR) deve recolher informaçÔes de comportamentos, personalidade, preferĂȘncias e outras informaçÔes contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferĂȘncias do utilizador e na anĂĄlise de traços fisiolĂłgicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restriçÔes (CSP). PropĂ”e-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turĂstico possĂvel para o utilizador final. A principal contribuição deste SR Ă© representar o modelo como CSP e tratĂĄ-lo como problema de otimização. ImplementĂĄmos o nosso sistema com o solucionador em Minizinc com restriçÔes de domĂnio representadas pelas preferĂȘncias dos utilizadores. O CSP permitiu que as preferĂȘncias dos utilizadores guiassem o sistema para encontrar as soluçÔes ideais
A Blockchain-Based Trust Management Framework with Verifiable Interactions
There has been tremendous interest in the development of formal trust models
and metrics through the use of analytics (e.g., Belief Theory and Bayesian
models), logics (e.g., Epistemic and Subjective Logic) and other mathematical
models. The choice of trust metric will depend on context, circumstance and
user requirements and there is no single best metric for use in all
circumstances. Where different users require different trust metrics to be
employed the trust score calculations should still be based on all available
trust evidence. Trust is normally computed using past experiences but, in
practice (especially in centralised systems), the validity and accuracy of
these experiences are taken for granted. In this paper, we provide a formal
framework and practical blockchain-based implementation that allows independent
trust providers to implement different trust metrics in a distributed manner
while still allowing all trust providers to base their calculations on a common
set of trust evidence. Further, our design allows experiences to be provably
linked to interactions without the need for a central authority. This leads to
the notion of evidence-based trust with provable interactions. Leveraging
blockchain allows the trust providers to offer their services in a competitive
manner, charging fees while users are provided with payments for recording
experiences. Performance details of the blockchain implementation are provided
Using Semantic-Based User Profile Modeling for Context-Aware Personalised Place Recommendations
Place Recommendation Systems (PRS's) are used to recommend places to visit to World Wide Web users. Existing PRS's are still limited by several problems, some of which are the problem of recommending similar set of places to different users (Lack of Personalization) and no diversity in the set of recommended items (Content Overspecialization). One of the main objectives in the PRS's or Contextual suggestion systems is to fill the semantic gap among the queries and suggestions and going beyond keywords matching. To address these issues, in this study we attempt to build a personalized context-aware place recommender system using semantic-based user profile modeling to address the limitations of current user profile building techniques and to improve the retrieval performance of personalized place recommender system. This approach consists of building a place ontology based on the Open Directory Project (ODP), a hierarchical ontology scheme for organizing websites. We model a semantic user profile from the place concepts extracted from place ontology and weighted according to their semantic relatedness to user interests. The semantic user profile is then exploited to devise a personalized recommendation by re-ranking process of initial search results for improving retrieval performance. We evaluate this approach on dataset obtained using Google Paces API. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based place recommendation model
Which Factors Determine Userâs First and Repeat Online Music Listening Respectively? Music Itself, User Itself, or Online Feedback
In the era of Web 2.0, does online feedback mainly dominant online usersâ buying behavior, or are userâs own preference and product quality still important? Previous studies paid more attention to the influence of online feedback on usersâ online buying behavior, however this paper focuses on how usersâ own factors, product quality related factors and online feedback factors together influence a userâs buying behavior, and also how does this effect change as time goes by. Taking online music as our research industry and using the data from Last.fm website, this research shows that usersâ preference and product quality are still the two most dominate factors influencing usersâ online music listening, while online feedback plays an important role on usersâ first listening. It is also found that the different influences of crowds and friends
Social software for music
Tese de mestrado integrado. Engenharia Informåtica e Computação. Faculdade de Engenharia. Universidade do Porto. 200
Personalised privacy in pervasive and ubiquitous systems
Our world is edging closer to the realisation of pervasive systems and their integration in our everyday life. While pervasive systems are capable of offering many benefits for everyone, the amount and quality of personal information that becomes available raise concerns about maintaining user privacy and create a real need to reform existing privacy practices and provide appropriate safeguards for the user of pervasive environments.
This thesis presents the PERSOnalised Negotiation, Identity Selection and Management (PersoNISM) system; a comprehensive approach to privacy protection in pervasive environments using context aware dynamic personalisation and behaviour learning. The aim of the PersoNISM system is twofold: to provide the user with a comprehensive set of privacy protecting tools and to help them make the best use of these tools according to their privacy needs. The PersoNISM system allows users to: a) configure the terms and conditions of data disclosure through the process of privacy policy negotiation, which addresses the current âtake it or leave itâ approach; b) use multiple identities to interact with pervasive services to avoid the accumulation of vast amounts of personal information in a single user profile; and c) selectively disclose information based on the type of information, who requests it, under what context, for what purpose and how the information will be treated. The PersoNISM system learns user privacy preferences by monitoring the behaviour of the user and uses them to personalise and/or automate the decision making processes in order to unburden the user from manually controlling these complex mechanisms.
The PersoNISM system has been designed, implemented, demonstrated and evaluated during three EU funded projects
Nudging according to userâs preferences
Physical inactivity has been identified as a global pandemic, physical inactivity causes multiple health outcomes in different demographic group such as coronary heart disease, type 2 diabetes, colon, and breast cancer. A physical inactive person takes less than 5000 steps a day. To try to reduce physical inactivity along individual for healthy lifestyle, this thesis provides personalized digital nudge. Nudge means to guide someone to do something that is beneficial for the long-term benefit of the person being nudged and doing so using UI (user interface) in digital environment is known as digital nudging. As people are relying more on technology for their decision making, the information collected from the integration of the devices is used to provide personalized nudges. As people have access to smartphones and wearable devices, data is collected from these devices to provide tailored nudges to achieve minimum required steps to reduce inactivity. Personalized nudge is a smart nudge which predictably influence people's behaviour. It is a type of digital nudge. This kind of nudge takes userâs information into account before nudging a user. This thesis also provides recommendations (new activities) based on personâs preference. The presented system was also tested by real users, and the feedback suggested that the presented system indeed urged them to be more active
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