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  • 1.
    Sahlin, Johannes
    et al.
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT. School of Informatics, University of Skövde.
    Sundell, Håkan
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Mbiydzenyuy, Gideon
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Alm, Håkan
    Högskolan i Borås, Akademin för textil, teknik och ekonomi. School of Informatics, University of Skövde.
    Holgersson, Jesper
    School of Informatics, University of Skövde.
    Suhonen, Christoffer
    Department of Information Technology, University of Borås.
    Hjelm, Tommy
    Department of Information Technology, University of Borås.
    Exploring Consumers' Discernment Ability of Autogenerated Advertisements2023Ingår i: Machine Learning, Multi Agent and Cyber Physical Systems: Proceedings of the 15th International FLINS Conference (FLINS 2022) / [ed] Qinglin Sun; Jie Lu; Xianyi Zeng; Etienne E. Kerre; Tianrui Li, World Scientific, 2023, s. 322-329Konferensbidrag (Refereegranskat)
    Abstract [en]

    Autogenerated Advertisements (AGAs) can be a concern for consumers if they suspect that Artificial Intelligence (AI) was involved. Consumers may have an opposing stance against AI, leading companies to miss profit opportunities and reputation loss. Hence, companies need ways of managing consumers’ con-cerns. As a part of designing such advices we explore consumers’ discernment ability (DA) of AGAs. A quantitative survey was used to explore consumers’ DA of AGAs. In order to do this, we administered questionnaires to 233 re-spondents. A statistical analysis including Z-tests, of these responses suggests that consumers can hardly pick out AGAs. This indicates that consumers may be guessing and thus do not possess any significant DA of our AGAs.

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  • 2.
    Sahlin, Johannes
    et al.
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT. School of Informatics, University of Skövde.
    Sundell, Håkan
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Mbiydzenyuy, Gideon
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Holgersson, Jesper
    School of Informatics, University of Skövde.
    Scoped Literature Review of Artificial Intelligence Marketing Adoptions for Ad Optimization with Reinforcement Learning2023Ingår i: Machine Learning, Multi Agent and Cyber Physical Systems: Proceedings of the 15th International FLINS Conference (FLINS 2022), World Scientific, 2023, s. 416-423Konferensbidrag (Refereegranskat)
    Abstract [en]

    Artificial Intelligence (AI) and Machine Learning (ML) are shaping marketing activities through digital innovations. Competition is a familiar concept for any digital retailer, and the digital transformation provides hopes for gaining a competitive edge over competitors. Those who do not adopt digital innovations risk getting outcompeted by those who do. This study aims to identify AI mar-keting (AIM) adoptions used for ad optimization with Reinforcement Learning (RL). A scoped literature review is used to find ad optimization adoptions re-search trends with RL in AIM. Scoping this is important both to research and practice as it provides spots for novel adaptations and directions of research of digital ad optimization with RL. The results of the review provide several differ-ent adoptions of ad optimization with RL in AIM. In short, the major category is Ad Relevance Optimization that takes several different forms depending on the purpose of the adoption. The underlying found themes of adoptions are Ad Attractiveness, Edge Ad, Sequential Ad and Ad Criteria Optimization. In conclusion, AIM adoptions with RL is scarce, and recommendations for future research are suggested based on the findings of the review.

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  • 3.
    Mbiydzenyuy, Gideon
    et al.
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Nowaczyk, Sławomir
    CAISR, University of Halmstad, SE-301 18 Halmstad, Sweden.
    Knutsson, Håkan
    The School of Business, Engineering and Science, University of Halmstad, SE-301 18 Halmstad, Sweden.
    Vanhoudt, Dirk
    VITO, Boeretang 200, 2400 Mol, Belgium.
    Brage, Jens
    NODA Intelligent Systems, SE-374 35 Karlshamn, Sweden.
    Calikus, Ece
    CAISR, University of Halmstad, SE-301 18 Halmstad, Sweden.
    Opportunities for Machine Learning in District Heating2021Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 11, nr 13Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry.

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  • 4.
    Annavarjula, Vaishnavi
    et al.
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Mbiydzenyuy, Gideon
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Riveiro, Maria
    Jönköpings Universitet.
    Lavesson, Niklas
    Jönköpings Universitet.
    Implicit user data in fashion recommendation systems2020Ingår i: Developments of Artificial Intelligence Technologies in Computation and Robotics, WORLD SCIENTIFIC , 2020Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Recommendation systems in fashion are used to provide recommendations to users on clothing items, matching styles, and size or fit. These recommendations are generated based on user actions such as ratings, reviews or general interaction with a seller. There is an increased adoption of implicit feedback in models aimed at providing recommendations in fashion. This paper aims to understand the nature of implicit user feedback in fashion recommendation systems by following guidelines to group user actions. Categories of user actions that characterize implicit feedback are examination, retention, reference, and annotation. Each category describes a specific set of actions a user takes. It is observed that fashion recommendations using implicit user feedback mostly rely on retention as a user action to provide recommendations.

  • 5.
    Mbiydzenyuy, Gideon
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Univariate Time Series Anomaly Labelling Algorithm2020Ingår i: Machine Learning, Optimization, and Data Science / [ed] Nicosia, Giuseppe; Ojha, Varun; La Malfa, Emanuele; Jansen, Giorgio; Sciacca, Vincenzo; Pardalos, Panos; Giuffrida, Giovanni; Umeton, Renato, Cham.: Springer Publishing Company, 2020, s. 586-599Konferensbidrag (Refereegranskat)
    Abstract [en]

    Unsupervised anomaly detection in an n-length univariate time series often comes with high risk. Anomaly contextual dependencies limit the application of binary classification methods. Analyzing the statistical features of data may help enrich the context of anomaly detection. This article proposes a quadratic time algorithm for analyzing possible anomalies in the context of unsupervised learning. Detection of possible anomalies uses Median Absolute Deviation on the residual of a univariate time series. Computation of residuals uses robust STL (Seasonal and Trend decomposition using Loess). Experiments on three datasets (Yahoo, NUMENTA NAB and district-heating substation power profiles) show the ability of the algorithm to enrich anomalies by associating labels such as Certainty, Uncertainty, and Probable, with the probable class indicating a need to further process the anomalies.

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