2,222 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
In recent years, the tourism industry has witnessed substantial growth, thanks to the pro liferation of digital technology and online platforms. Tourists now have greater access to
information and the ability to make informed travel decisions. However, the abundance
of available information often leaves tourists overwhelmed when selecting points of inter est (POI) that align with their preferences. Recommender Systems (RS) have emerged as
a solution, personalising recommendations based on tourist behaviour, social networks, and
contextual factors. To enhance RS efficacy, researchers have begun exploring the integration
of psychological factors, such as personality traits. Yet, to meet the demands of modern
tourists, a robust knowledge base, such as a tourist attractions ontology, is essential for
seamless and rapid matching of tourist characteristics and preferences with available POI.
With that in mind, this project aims to enhance a Group Recommender System (GRS)
prototype, GrouPlanner, by creating a robust tourist attractions ontology. This ontology
will facilitate rapid and accurate matching of points of interest with tourists’ character istics, including personality, preferences, and demographic data, ultimately improving POI
recommendations.
First, there needs to be an understanding of the personality of tourists and how it influences
their choices when it comes to picking the best point of interest based on their personality.
With that knowledge acquired, it is time to choose a way to represent this knowledge in the
form of an ontology.
In this project, the Protégé ontology editor was used to design the ontology and the rela tionships between the tourists’ personality and the points of interest. After designing the
ontology, it had to be converted to a database so the Grouplanner system could access it.
So, to do that, a solution was designed to integrate the designed ontology in a triple store
data base, in this case, Apache Fuseki.
With the database implemented, several tests were made to verify if the database would
give the recommended points of interests based on the tourists’ preferences. This tests were
later analysed.Nos anos mais recentes, a indĂşstria do turismo presenciou um crescimento substancial dev ido Ă tecnologia digital e plataformas online. Cada vez mais, os turistas tĂŞm acesso a uma
abundância de informação que influencia a habilidade de tomar decisões sobre viajar. No
entanto, esta informação pode complicar a seleção dos pontos de interesse que alinhem com
as preferências dos turistas. Para combater isso, sistemas de recomendação (SR) emergi ram como uma solução, personalizando as recomendações com base no comportamento do
turista, redes socias e outros fatores. Para aumentar a eficácia destes sistemas, os investi gadores começaram a explorar a possibilidade de integração com fatores psicológicos, como
traços de personalidade. Apesar disso, para cumprir as exigências dos turistas modernos,
uma base de conhecimento robusta, como uma ontologia de atrações turĂsticas, Ă© essencial
para, de forma eficaz e eficiente, corresponder as caracterĂsticas dos turistas com os pontos
de interesse disponĂveis.
Com isso em mente, este projeto tem como objetivo melhorar um protĂłtipo de um sistema
de recomendação (GrouPlanner), criando uma ontologia robusta de atrações turĂsticas. Essa
ontologia facilitará a correspondĂŞncia rápida e precisa de pontos de interesse com as car acterĂsticas dos turistas, incluindo a sua personalidade e as suas preferĂŞncias, melhorando
assim as recomendações de pontos de interesse.
Em primeiro lugar, é necessário compreender a personalidade dos turistas e como ela influ encia as suas escolhas ao selecionar o melhor ponto de interesse com base na sua person alidade. Com esse ponto adquirido, é necessário escolher uma maneira de representar esse
conhecimento na forma de uma ontologia.
Neste projeto, o editor de ontologias Protégé foi utilizado para projetar a ontologia e as
relações entre a personalidade dos turistas e os pontos de interesse. Após a construção da
ontologia, foi necessário convertê-la numa base de dados para que o sistema Grouplanner
pudesse ter acesso. Para isso, foi desenhada uma solução para integrar a ontologia projetada
numa base de dados "triple store", neste caso, o Apache Fuseki.
Com a base de dados implementada, foram realizados vários testes para verificar se esta
forneceria os pontos de interesse recomendados com base nas preferĂŞncias dos turistas.
Esses testes foram depois analisados
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The influence of national culture on the attitude towards mobile recommender systems
This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation
of Korea Grant funded by the Korean governmen
An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus
The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique
Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal
The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented
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
Collaborative-demographic hybrid for financial: product recommendation
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM
processes, several financial institutions are striving to leverage customer data and integrate insights
regarding customer behaviour, needs, and preferences into their marketing approach. As decision
support systems assisting marketing and commercial efforts, Recommender Systems applied to the
financial domain have been gaining increased attention. This thesis studies a Collaborative-
Demographic Hybrid Recommendation System, applied to the financial services sector, based on real
data provided by a Portuguese private commercial bank. This work establishes a framework to support
account managers’ advice on which financial product is most suitable for each of the bank’s corporate
clients. The recommendation problem is further developed by conducting a performance comparison
for both multi-output regression and multiclass classification prediction approaches. Experimental
results indicate that multiclass architectures are better suited for the prediction task, outperforming
alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass
Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming
algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving
corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study
provides important contributions for positioning the bank’s commercial efforts around customers’
future requirements. By allowing for a better understanding of customers’ needs and preferences, the
proposed Recommender allows for more personalized and targeted marketing contacts, leading to
higher conversion rates, corporate profitability, and customer satisfaction and loyalty
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