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Smedberg, H., Bandaru, S., Riveiro, M. & Ng, A. H. C. (2023). Mimer: A web-based tool for knowledge discovery in multi-criteria decision support. IEEE Computational Intelligence Magazine
Open this publication in new window or tab >>Mimer: A web-based tool for knowledge discovery in multi-criteria decision support
2023 (English)In: IEEE Computational Intelligence Magazine, ISSN 1556-603X, E-ISSN 1556-6048Article in journal (Other academic) Submitted
National Category
Computer Sciences Information Systems Software Engineering Computer Systems Computational Mathematics
Research subject
Virtual Production Development (VPD); Skövde Artificial Intelligence Lab (SAIL); VF-KDO
Identifiers
urn:nbn:se:his:diva-23154 (URN)
Funder
Knowledge Foundation, 2018-0011
Note

This work was funded by the Knowledge Foundation (KKS), Sweden, through the KKS Profile, Virtual Factories with Knowledge-Driven Optimization (VF-KDO), Grant No. 2018-0011.

Available from: 2023-09-01 Created: 2023-09-01 Last updated: 2023-09-11Bibliographically approved
Ohlson, N.-E., Riveiro, M. & Bäckstrand, J. (2022). Identification of tasks to be supported by machine learning to reduce Sales & Operations Planning challenges in an engineer-to-order context. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish production symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), School of Engineering Science, University of Skövde, Sweden, April 26–29 2022 (pp. 39-50). Amsterdam: IOS Press
Open this publication in new window or tab >>Identification of tasks to be supported by machine learning to reduce Sales & Operations Planning challenges in an engineer-to-order context
2022 (English)In: SPS2022: Proceedings of the 10th Swedish production symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam: IOS Press, 2022, p. 39-50Conference paper, Published paper (Refereed)
Abstract [en]

Sales and Operations Planning (S&OP) is a process that aims to align dimensioning efforts in a company, based on one integrated plan and with clear decision milestones. The alignment is cross-functional and connects different operations functions with each other to set an overall delivery ability. There are always challenges connecting different functions in a company which most S&OP practitioners agree with, still, that is one of the things that the S&OP-process should bridge. Digital solutions such as Enterprise Resource Planning (ERP) and other more or less sophisticated tools have contributed to an improved cross functional communication over time. S&OP in an Engineer-to-order (ETO) context, especially where engineering is a major or an equal portion as e.g., make-to-stock (MTS) and make-to-order (MTO) contexts, may experience even further challenges. Technologies within Industry 4.0 are changing the way S&OP is carried out; one of the most relevant ones is Artificial Intelligence (AI), particularly, Machine Learning (ML) that analyses data collected during these processes to find patterns and extract knowledge. The intent with this paper is to, based on S&OP-challenges, see if ML can be used to improve these challenges.

In a brief literature review together with empiric data from a single industrial case (SIC), S&OP-challenges were defined and structured. Based on the challenges in several S&OP-sub-areas, classified into data quality, horizontal and vertical disconnects, specific tasks were specified and structured into anomaly detection, clustering and classification, and predictions. Which exact ML-method to use require further work and tests. Still, this is a good starting point to take the next step and the specified tasks could also be used for other practitioners that want to start using ML/AI in their daily activities.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2022
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords
Sales & Operations Planning, Engineer to Order, Machine Learning
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:his:diva-22302 (URN)10.3233/ATDE220124 (DOI)2-s2.0-85132814053 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium (SPS2022), School of Engineering Science, University of Skövde, Sweden, April 26–29 2022
Note

CC BY-NC 4.0

Corresponding Author, Nils-Erik Ohlson, Jönköping University, School of Engineering, Gjuterigatan 5, SE 553 18 Jönköping, Sweden, E-mail: nilserik.ohlson@ju.se

VF-KDO

Available from: 2022-05-02 Created: 2023-02-24 Last updated: 2023-02-28
Ohlson, N.-E., Bäckstrand, J. & Riveiro, M. (2021). Artificial Intelligence-enhanced Sales & Operations Planning in an Engineer-to-order context. In: : . Paper presented at PLANs forsknings- och tillämpningskonferens 2021, Högskolan i Borås, 20-21 oktober 2021.
Open this publication in new window or tab >>Artificial Intelligence-enhanced Sales & Operations Planning in an Engineer-to-order context
2021 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Sales and Operations Planning (S&OP) is a process that aims to align dimensioning efforts in a company, based on the "One Plan" and with clear decision milestones, where “One Plan” relates to the ultimate outcome of S&OP by integrating multiple plans. This alignment is cross functional and connects, not only sales and operations, but also different operations functions with each other, to set an overall delivery ability. There are always challenges when connecting different functions in a company, something most S&OP practitioners agree with, still, cross functional integration is one of the things that the S&OP-process addresses. For S&OP in an Engineer-to-order (ETO) context, especially where engineering is a major or an equal portion of the product as e.g., make-to-stock (MTS) or make-to-order (MTO) contexts, further complexity is added. If these businesses also have long lead times and low volumes, another perspective to the S&OP-process is given when it comes to the balance between demand and supply (DS). Digital solutions such as Enterprise Resource Planning (ERP) and other more or less sophisticated tools are a pre-requisite for the S&OP-process and improves cross functional integration. Technologies within Industry 4.0 are changing the way S&OP is carried out; one of the most relevant one is Artificial Intelligence (AI), particularly, Machine Learning (ML) that analyses data collected during these processes to find patterns and extract knowledge.

 Therefore, in this paper, the purpose is to investigate and define the main sub-areas of the S&OP-process in an ETO-context and discuss how AI, in particular ML, currently supports the sub-areas. To be able to fulfil the purpose, a literature study of the two main fields, S&OP and AI, has been carried out.

 The results are pointing at an underuse of ML-techniques for S&OP. Forecasting in MTS- context is where ML is mostly used, and the most common ML-technique is Artificial Neutral Networks (ANN) which is considered as Supervised Learning. The results of this paper will serve as a starting point for further research on the efforts and effects required for improving the S&OP-process in an ETO-context and with what ML-techniques.

National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:his:diva-22299 (URN)
Conference
PLANs forsknings- och tillämpningskonferens 2021, Högskolan i Borås, 20-21 oktober 2021
Available from: 2022-01-18 Created: 2023-02-24 Last updated: 2023-02-28Bibliographically approved
Ulfenborg, B., Karlsson, A., Riveiro, M., Andersson, C. X., Sartipy, P. & Synnergren, J. (2021). Multi-Assignment Clustering: Machine learning from a biological perspective. Journal of Biotechnology, 326, 1-10
Open this publication in new window or tab >>Multi-Assignment Clustering: Machine learning from a biological perspective
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2021 (English)In: Journal of Biotechnology, ISSN 0168-1656, E-ISSN 1873-4863, Vol. 326, p. 1-10Article in journal (Refereed) Published
Abstract [en]

A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Clustering, K-means, annotation enrichment, multiple cluster assignment, pathways, transcriptomics
National Category
Bioinformatics and Systems Biology
Research subject
Bioinformatics; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-19329 (URN)10.1016/j.jbiotec.2020.12.002 (DOI)000616124700001 ()33285150 (PubMedID)2-s2.0-85097644109 (Scopus ID)
Note

CC BY 4.0

Available from: 2020-12-16 Created: 2020-12-16 Last updated: 2021-03-11Bibliographically approved
Ventocilla, E., Martins, R. M., Paulovich, F. & Riveiro, M. (2021). Scaling the Growing Neural Gas for Visual Cluster Analysis. Big Data Research, 26, Article ID 100254.
Open this publication in new window or tab >>Scaling the Growing Neural Gas for Visual Cluster Analysis
2021 (English)In: Big Data Research, ISSN 2214-5796, E-ISSN 2214-580X, Vol. 26, article id 100254Article in journal (Refereed) Published
Abstract [en]

The growing neural gas (GNG) is an unsupervised topology learning algorithm that models a data space through interconnected units that stand on the populated areas of that space. Its output is a graph that can be visually represented on a two-dimensional plane, and be used as means to disclose cluster patterns in datasets. GNG, however, creates highly connected graphs when trained on high dimensional data, which in turn leads to highly clutter representations that fail to disclose any meaningful patterns. Moreover, its sequential learning limits its potential for faster executions on local datasets, and, more importantly, its potential for training on distributed datasets while leveraging from the computational resources of the infrastructures in which they reside.

This paper presents two methods that improve GNG for the visualization of cluster patterns in large and high-dimensional datasets. The first one focuses on providing more meaningful and accurate cluster pattern representations of high-dimensional datasets, by avoiding connections that lead to high-dimensional graphs in the modeled topology, which may, in turn, lead to visual cluttering in 2D representations. The second method presented in this paper enables the use of GNG on big and distributed datasets with faster execution times, by modeling and merging separate parts of a dataset using the MapReduce model.

Quantitative and qualitative evaluations show that the first method leads to the creation of lower-dimensional graph structures, which in turn provide more accurate and meaningful cluster representations; and that the second method preserves the accuracy and meaning of the cluster representations while enabling its execution in distributed settings.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Growing neural gas, clustering, cluster patterns, visualization, mapreduce
National Category
Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL); VF-KDO
Identifiers
urn:nbn:se:his:diva-19460 (URN)10.1016/j.bdr.2021.100254 (DOI)000710458600012 ()2-s2.0-85113545584 (Scopus ID)
Note

CC BY 4.0

Available from: 2021-02-10 Created: 2021-02-10 Last updated: 2023-02-22Bibliographically approved
Beauxis-Aussalet, E., Behrisch, M., Borgo, R., Chau, D. H., Collins, C., Ebert, D., . . . van Wijk, J. J. (2021). The Role of Interactive Visualization in Fostering Trust in AI. IEEE Computer Graphics and Applications, 41(6), 7-12
Open this publication in new window or tab >>The Role of Interactive Visualization in Fostering Trust in AI
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2021 (English)In: IEEE Computer Graphics and Applications, ISSN 0272-1716, E-ISSN 1558-1756, Vol. 41, no 6, p. 7-12Article in journal (Refereed) Published
Abstract [en]

The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.

Place, publisher, year, edition, pages
IEEE, 2021
National Category
Computer Sciences Human Aspects of ICT
Identifiers
urn:nbn:se:his:diva-22304 (URN)10.1109/MCG.2021.3107875 (DOI)000728925900008 ()34890313 (PubMedID)2-s2.0-85121671133 (Scopus ID);intsam;784752 (Local ID);intsam;784752 (Archive number);intsam;784752 (OAI)
Note

VF-KDO

This Viewpoint is the result of discussion at Dagstuhl Seminar on Interactive Visualization for Fostering Trust in AI (seminar 22351).

Contact department editor Theresa-Marie Rhyne at theresamarierhyne@gmail.com.

Available from: 2021-12-16 Created: 2023-02-24 Last updated: 2023-02-28Bibliographically approved
Schuller, B. W., Virtanen, T., Riveiro, M., Rizos, G., Han, J., Mesaros, A. & Drossos, K. (2021). Towards Sonification in Multimodal and User-Friendly Explainable Artificial Intelligence. In: Zakia Hammal; Carlos Busso; Catherine Pelachaud; Sharon Oviatt; Albert Ali Salah; Guoying Zhao (Ed.), ICMI '21: Proceedings of the 2021 International Conference on Multimodal Interaction. Paper presented at ICMI ’21, International Conference on Multimodal Interaction, 18–22 October, Montréal, QC, Canada (pp. 788-792). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Towards Sonification in Multimodal and User-Friendly Explainable Artificial Intelligence
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2021 (English)In: ICMI '21: Proceedings of the 2021 International Conference on Multimodal Interaction / [ed] Zakia Hammal; Carlos Busso; Catherine Pelachaud; Sharon Oviatt; Albert Ali Salah; Guoying Zhao, Association for Computing Machinery (ACM), 2021, p. 788-792Conference paper, Published paper (Refereed)
Abstract [en]

We are largely used to hearing explanations. For example, if someone thinks you are sad today, they might reply to your “why?” with “because you were so Hmmmmm-mmm-mmm”. Today’s Artificial Intelligence (AI), however, is – if at all – largely providing explanations of decisions in a visual or textual manner. While such approaches are good for communication via visual media such as in research papers or screens of intelligent devices, they may not always be the best way to explain; especially when the end user is not an expert. In particular, when the AI’s task is about Audio Intelligence, visual explanations appear less intuitive than audible, sonified ones. Sonification has also great potential for explainable AI (XAI) in systems that deal with non-audio data – for example, because it does not require visual contact or active attention of a user. Hence, sonified explanations of AI decisions face a challenging, yet highly promising and pioneering task. That involves incorporating innovative XAI algorithms to allow pointing back at the learning data responsible for decisions made by an AI, and to include decomposition of the data to identify salient aspects. It further aims to identify the components of the preprocessing, feature representation, and learnt attention patterns that are responsible for the decisions. Finally, it targets decision-making at the model-level, to provide a holistic explanation of the chain of processing in typical pattern recognition problems from end-to-end. Sonified AI explanations will need to unite methods for sonification of the identified aspects that benefit decisions, decomposition and recomposition of audio to sonify which parts in the audio were responsible for the decision, and rendering attention patterns and salient feature representations audible. Benchmarking sonified XAI is challenging, as it will require a comparison against a backdrop of existing, state-of-the-art visual and textual alternatives, as well as synergistic complementation of all modalities in user evaluations. Sonified AI explanations will need to target different user groups to allow personalisation of the sonification experience for different user needs, to lead to a major breakthrough in comprehensibility of AI via hearing how decisions are made, hence supporting tomorrow’s humane AI’s trustability. Here, we introduce and motivate the general idea, and provide accompanying considerations including milestones of realisation of sonifed XAI and foreseeable risks.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2021
Keywords
Explainable artificial intelligence, sonification, trustworthy artificial intelligence, human computer interaction, multimodality
National Category
Computer Sciences Media Engineering Human Computer Interaction
Identifiers
urn:nbn:se:his:diva-22301 (URN)10.1145/3462244.3479879 (DOI)2-s2.0-85118971526 (Scopus ID)978-1-4503-8481-0 (ISBN)
Conference
ICMI ’21, International Conference on Multimodal Interaction, 18–22 October, Montréal, QC, Canada
Funder
EU, Horizon 2020, 826506
Note

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agree-ment No. 826506 (sustAGE).

VF-KDO

Available from: 2021-10-21 Created: 2023-02-24 Last updated: 2023-02-28Bibliographically approved
Ventocilla, E. & Riveiro, M. (2020). A comparative user study of visualization techniques for cluster analysis of multidimensional data sets. Information Visualization, 19(4), 318-338
Open this publication in new window or tab >>A comparative user study of visualization techniques for cluster analysis of multidimensional data sets
2020 (English)In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 19, no 4, p. 318-338Article in journal (Refereed) Published
Abstract [en]

This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters, k, embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of k as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with k values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward's hierarchical clustering) are likely to lead to estimates of k that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability.

Place, publisher, year, edition, pages
Sage Publications, 2020
Keywords
Cluster patterns, visualization, data structure, user study, multidimensional data
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); VF-KDO
Identifiers
urn:nbn:se:his:diva-18851 (URN)10.1177/1473871620922166 (DOI)000545375800001 ()2-s2.0-85087437127 (Scopus ID)
Note

CC BY

Available from: 2020-07-20 Created: 2020-07-20 Last updated: 2023-02-22Bibliographically approved
Ventocilla, E. & Riveiro, M. (2020). A Model for the Progressive Visualization of Multidimensional Data Structure. In: Ana Paula Cláudio; Kadi Bouatouch; Manuela Chessa; Alexis Paljic; Andreas Kerren; Christophe Hurter; Alain Tremeau; Giovanni Maria Farinella (Ed.), Ana Paula Cláudio, Kadi Bouatouch, Manuela Chessa, Alexis Paljic, Andreas Kerren, Christophe Hurter, Alain Tremeau, Giovanni Maria Farinella (Ed.), Computer Vision, Imaging and Computer Graphics Theory and Applications: 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25–27, 2019, Revised Selected Papers. Paper presented at 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019; Prague; Czech Republic; 25 February 2019 through 27 February 2019; Code 237909 (pp. 203-226). Paper presented at 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019; Prague; Czech Republic; 25 February 2019 through 27 February 2019; Code 237909. Cham: Springer, 1182
Open this publication in new window or tab >>A Model for the Progressive Visualization of Multidimensional Data Structure
2020 (English)In: Computer Vision, Imaging and Computer Graphics Theory and Applications: 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25–27, 2019, Revised Selected Papers / [ed] Ana Paula Cláudio; Kadi Bouatouch; Manuela Chessa; Alexis Paljic; Andreas Kerren; Christophe Hurter; Alain Tremeau; Giovanni Maria Farinella, Cham: Springer, 2020, Vol. 1182, p. 203-226Chapter in book (Refereed)
Abstract [en]

This paper presents a model for the progressive visualization and exploration of the structure of large datasets. That is, an abstraction on different components and relations which provide means for constructing a visual representation of a dataset’s structure, with continuous system feedback and enabled user interactions for computational steering, in spite of size. In this context, the structure of a dataset is regarded as the distance or neighborhood relationships among its data points. Size, on the other hand, is defined in terms of the number of data points. To prove the validity of the model, a proof-of-concept was developed as a Visual Analytics library for Apache Zeppelin and Apache Spark. Moreover, nine user studies where carried in order to assess the usability of the library. The results from the user studies show that the library is useful for visualizing and understanding the emerging cluster patterns, for identifying relevant features, and for estimating the number of clusters. 

Place, publisher, year, edition, pages
Cham: Springer, 2020
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1182
Keywords
Data structure, Exploratory data analysis, Growing neural gas, Large data analysis, Multidimensional data, Multidimensional projection, Progressive visualization, Visual analytics, Computation theory, Computer vision, Data handling, Data structures, Information analysis, Large dataset, Petroleum prospecting, Visualization, Data visualization
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); VF-KDO
Identifiers
urn:nbn:se:his:diva-18343 (URN)10.1007/978-3-030-41590-7_9 (DOI)000659188700009 ()2-s2.0-85081622039 (Scopus ID)978-3-030-41589-1 (ISBN)978-3-030-41590-7 (ISBN)
Conference
14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019; Prague; Czech Republic; 25 February 2019 through 27 February 2019; Code 237909
Available from: 2020-03-26 Created: 2020-03-26 Last updated: 2023-02-24Bibliographically approved
Annavarjula, V., Mbiydzenyuy, G., Riveiro, M. & Lavesson, N. (2020). Implicit user data in fashion recommendation systems. In: Li Zhong; Chunrong Yuan; Jie Lu; Etienne E. Kerre (Ed.), Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020). Paper presented at 15th Symposium of Intelligent Systems and Knowledge Engineering (ISKE) held jointly with 14th International FLINS Conference (FLINS 2020), Cologne, Germany, 18 – 21 August 2020 (pp. 614-621). World Scientific
Open this publication in new window or tab >>Implicit user data in fashion recommendation systems
2020 (English)In: Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020) / [ed] Li Zhong; Chunrong Yuan; Jie Lu; Etienne E. Kerre, World Scientific, 2020, p. 614-621Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
World Scientific, 2020
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, ISSN 1793-7868 ; 12
Keywords
Recommendation Systems, Fashion, Implicit User Feedback
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-19979 (URN)10.1142/9789811223334_0074 (DOI)000656123200074 ()978-981-122-333-4 (ISBN)978-981-122-334-1 (ISBN)
Conference
15th Symposium of Intelligent Systems and Knowledge Engineering (ISKE) held jointly with 14th International FLINS Conference (FLINS 2020), Cologne, Germany, 18 – 21 August 2020
Available from: 2021-06-24 Created: 2021-06-24 Last updated: 2021-09-13Bibliographically approved
Projects
Virtual factories with knowledge-driven optimization (VF-KDO); University of Skövde; Publications
Nourmohammadi, A., Fathi, M. & Ng, A. H. C. (2024). Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration. Computers & industrial engineering, 187, Article ID 109775. Lind, A., Iriondo Pascual, A., Hanson, L., Högberg, D., Lämkull, D. & Syberfeldt, A. (2024). Multi-objective optimisation of a logistics area in the context of factory layout planning. Production & Manufacturing Research, 12(1), Article ID 2323484. Barrera Diaz, C. A., Nourmohammadi, A., Smedberg, H., Aslam, T. & Ng, A. H. C. (2023). An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems. Mathematics, 11(6), Article ID 1527. Lind, A., Hanson, L., Högberg, D., Lämkull, D., Mårtensson, P. & Syberfeldt, A. (2023). Digital support for rules and regulations when planning and designing factory layouts. Procedia CIRP, 120, 1445-1450Redondo Verdú, C., Sempere Maciá, N., Strand, M., Holm, M., Schmidt, B. & Olsson, J. (2023). Enhancing Manual Assembly Training using Mixed Reality and Virtual Sensors. Paper presented at 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '23, Gulf of Naples, Italy, 12 - 14 July 2023. Procedia CIRPLind, A., Hanson, L., Högberg, D., Lämkull, D. & Syberfeldt, A. (2023). Extending and demonstrating an engineering communication framework utilising the digital twin concept in a context of factory layouts. International Journal of Services Operations and Informatics, 12(3), 201-224Danielsson, O., Syberfeldt, A., Holm, M. & Thorvald, P. (2023). Integration of Augmented Reality Smart Glasses as Assembly Support: A Framework Implementation in a Quick Evaluation Tool. International Journal of Manufacturing Research, 18(2), 144-164Smedberg, H. & Bandaru, S. (2023). Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization. European Journal of Operational Research, 306(3), 1311-1329Smedberg, H. (2023). Knowledge discovery for interactive decision support and knowledge-driven optimization. (Doctoral dissertation). Skövde: University of SkövdeSmedberg, H., Bandaru, S., Riveiro, M. & Ng, A. H. C. (2023). Mimer: A web-based tool for knowledge discovery in multi-criteria decision support. IEEE Computational Intelligence Magazine
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2900-9335

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