859 research outputs found
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
Information provision measures for voice agent product recommendationsâ The effect of process explanations and process visualizations on fairness perceptions
While voice agent product recommendations (VAPR) can be convenient for users, their underlying artificial intelligence (AI) components are subject to recommendation engine opacities and audio-based constraints, which limit usersâ information level when conducting purchase decisions. As a result, users might feel as if they are being treated unfairly, which can lead to negative consequences for retailers. Drawing from the information processing and stimulus-organism-response theory, we investigate through two experimental between subjects studies how process explanations and process visualizationsâas additional information provision measuresâaffect usersâ perceived fairness and behavioral responses to VAPRs. We find that process explanations have a positive effect on fairness perceptions, whereas process visualizations do not. Process explanations based on usersâ profiles and their purchase behavior show the strongest effects in improving fairness perceptions. We contribute to the literature on fair and explainable AI by extending the rather algorithm-centered perspectives by considering audio-based VAPR constraints and directly linking them to usersâ perceptions and responses. We inform practitioners how they can use information provision measures to avoid unjustified perceptions of unfairness and adverse behavioral responses
Managing Temporal Dynamics of Filter Bubbles
Filter bubbles have attracted much attention in recent years in terms of their impact on society. Whereas it is commonly agreed that filter bubbles should be managed, the question is still how. We draw a picture of filter bubbles as dynamic, slowly changing constructs that underlie temporal dynamics and that are constantly influenced by both machine and human. Anchored in a research setting with a major public broadcaster, we follow a design science approach on how to design the temporal dynamics in filter bubbles and how to design users' influence over time. We qualitatively evaluate our approach with a smartphone app for personalized radio and found that the adjustability of filter bubbles leads to a better co-creation of information flows between information broadcaster and listener
Visualization for Recommendation Explainability: A Survey and New Perspectives
Providing system-generated explanations for recommendations represents an
important step towards transparent and trustworthy recommender systems.
Explainable recommender systems provide a human-understandable rationale for
their outputs. Over the last two decades, explainable recommendation has
attracted much attention in the recommender systems research community. This
paper aims to provide a comprehensive review of research efforts on visual
explanation in recommender systems. More concretely, we systematically review
the literature on explanations in recommender systems based on four dimensions,
namely explanation goal, explanation scope, explanation style, and explanation
format. Recognizing the importance of visualization, we approach the
recommender system literature from the angle of explanatory visualizations,
that is using visualizations as a display style of explanation. As a result, we
derive a set of guidelines that might be constructive for designing explanatory
visualizations in recommender systems and identify perspectives for future work
in this field. The aim of this review is to help recommendation researchers and
practitioners better understand the potential of visually explainable
recommendation research and to support them in the systematic design of visual
explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page
Autoencoder-based Image Recommendation for Lung Cancer Characterization
Neste projeto, temos como objetivo desenvolver um sistema de IA que recomende um conjunto de casos relativos (passados) para orientar a tomada de decisão do médico.
Objetivo: A ambição Ă© desenvolver um modelo de aprendizado baseado em IA para caracterização de cĂąncer de pulmĂŁo, a fim de auxiliar na rotina clĂnica. Considerando a complexidade dos fenĂŽmenos biolĂłgicos que ocorrem durante o desenvolvimento do cĂąncer, as relaçÔes entre eles e as manifestaçÔes visuais capturadas pela tomografia computadorizada (CT) tĂȘm sido exploradas nos Ășltimos anos. No entanto, devido Ă falta de robustez dos mĂ©todos atuais de aprendizado profundo, essas correlaçÔes sĂŁo frequentemente consideradas espĂșrias e se perdem quando confrontadas com dados coletados a partir de distribuiçÔes alteradas: diferentes instituiçÔes, caracterĂsticas demogrĂĄficas ou atĂ© mesmo estĂĄgios de desenvolvimento do cĂąncer.In this project, we aim to develop an AI system that recommends a set of relative (past) cases to guide the decision-making of the clinician.
Objective: The ambition is to develop an AI-based learning model for lung cancer characterization in order to assist in clinical routine. Considering the complexity of the biological phenomenat hat occur during cancer development, relationships between these and visual manifestations captured by CT have been explored in recent years; however, given the lack of robustness of current deep learning methods, these correlations are often found spurious and get lost when facing data collected from shifted distributions: different institutions, demographics or even stages of cancer development
On intelligible multimodal visual analysis
Analyzing data becomes an important skill in a more and more digital world. Yet, many users are facing knowledge barriers preventing them to independently conduct their data analysis. To tear down some of these barriers, multimodal interaction for visual analysis has been proposed. Multimodal interaction through speech and touch enables not only experts, but also novice users to effortlessly interact with such kind of technology. However, current approaches do not take the user differences into account. In fact, whether visual analysis is intelligible ultimately depends on the user.
In order to close this research gap, this dissertation explores how multimodal visual analysis can be personalized. To do so, it takes a holistic view. First, an intelligible task space of visual analysis tasks is defined by considering personalization potentials. This task space provides an initial basis for understanding how effective personalization in visual analysis can be approached. Second, empirical analyses on speech commands in visual analysis as well as used visualizations from scientific publications further reveal patterns and structures. These behavior-indicated findings help to better understand expectations towards multimodal visual analysis. Third, a technical prototype is designed considering the previous findings. Enriching the visual analysis by a persistent dialogue and a transparency of the underlying computations, conducted user studies show not only advantages, but address the relevance of considering the userâs characteristics. Finally, both communications channels â visualizations and dialogue â are personalized. Leveraging linguistic theory and reinforcement learning, the results highlight a positive effect of adjusting to the user. Especially when the userâs knowledge is exceeded, personalizations helps to improve the user experience.
Overall, this dissertations confirms not only the importance of considering the userâs characteristics in multimodal visual analysis, but also provides insights on how an intelligible analysis can be achieved. By understanding the use of input modalities, a system can focus only on the userâs needs. By understanding preferences on the output modalities, the system can better adapt to the user. Combining both directions imporves user experience and contributes towards an intelligible multimodal visual analysis
Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System
Explainable recommender systems (RS) have traditionally followed a
one-size-fits-all approach, delivering the same explanation level of detail to
each user, without considering their individual needs and goals. Further,
explanations in RS have so far been presented mostly in a static and
non-interactive manner. To fill these research gaps, we aim in this paper to
adopt a user-centered, interactive explanation model that provides explanations
with different levels of detail and empowers users to interact with, control,
and personalize the explanations based on their needs and preferences. We
followed a user-centered approach to design interactive explanations with three
levels of detail (basic, intermediate, and advanced) and implemented them in
the transparent Recommendation and Interest Modeling Application (RIMA). We
conducted a qualitative user study (N=14) to investigate the impact of
providing interactive explanations with varying level of details on the users'
perception of the explainable RS. Our study showed qualitative evidence that
fostering interaction and giving users control in deciding which explanation
they would like to see can meet the demands of users with different needs,
preferences, and goals, and consequently can have positive effects on different
crucial aspects in explainable recommendation, including transparency, trust,
satisfaction, and user experience.Comment: 23 page
Adaptive Visualization for Focused Personalized Information Retrieval
The new trend on the Web has totally changed todays information access environment. The traditional information overload problem has evolved into the qualitative level beyond the quantitative growth. The mode of producing and consuming information is changing and we need a new paradigm for accessing information.Personalized search is one of the most promising answers to this problem. However, it still follows the old interaction model and representation method of classic information retrieval approaches. This limitation can harm the potential of personalized search, with which users are intended to interact with the system, learn and investigate the problem, and collaborate with the system to reach the final goal.This dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interac- tive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently.We extended a well-known visualization framework called VIBE (Visual Information Browsing Environment) and implemented Adaptive VIBE, so that it can fit into the per- sonalized searching environment. We tested the effectiveness of this adaptive visualization method and investigated its strengths and weaknesses by conducting a full-scale user study.We also tried to enrich the user models with named-entities considering the possibility that the traditional keyword-based user models could harm the effectiveness of the system in the context of interactive information retrieval.The results of the user study showed that the Adaptive VIBE could improve the precision of the personalized search system and could help the users to find out more diverse set of information. The named-entity based user model integrated into Adaptive VIBE showed improvements of precision of user annotations while maintaining the level of diverse discovery of information
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 managersâ advice on which financial product is most suitable for each of the bankâs 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 bankâs commercial efforts around customersâ
future requirements. By allowing for a better understanding of customersâ 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
Service-oriented Context-aware Framework
Location- and context-aware services are emerging technologies in mobile and
desktop environments, however, most of them are difficult to use and do not
seem to be beneficial enough. Our research focuses on designing and creating a
service-oriented framework that helps location- and context-aware,
client-service type application development and use. Location information is
combined with other contexts such as the users' history, preferences and
disabilities. The framework also handles the spatial model of the environment
(e.g. map of a room or a building) as a context. The framework is built on a
semantic backend where the ontologies are represented using the OWL description
language. The use of ontologies enables the framework to run inference tasks
and to easily adapt to new context types. The framework contains a
compatibility layer for positioning devices, which hides the technical
differences of positioning technologies and enables the combination of location
data of various sources
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