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  • 1.
    Bellogín, Alejandro
    et al.
    Iniversidad Autńoma de Madrid, Madrid, Spain.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Information Retrieval and Recommender Systems2019In: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 79-96Chapter in book (Refereed)
    Abstract [en]

    This chapter gives a brief introduction to what artificial intelligence is. We begin discussing some of the alternative definitions for artificial intelligence and introduce the four major areas of the field. Then, in subsequent sections we present these areas. They are problem solving and search, knowledge representation and knowledge-based systems, machine learning, and distributed artificial intelligence. The chapter follows with a discussion on some ethical dilemma we find in relation to artificial intelligence. A summary closes this chapter.

  • 2.
    Bellogín, Alejandro
    et al.
    Universidad Autónoma de Madrid, Madrid, Spain.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Recommender Systems Evaluation2018In: Encyclopedia of Social Network Analysis and Mining / [ed] Reda Alhajj, Jon Rokne, Springer, 2018, 2Chapter in book (Refereed)
  • 3.
    Bogers, Toine
    et al.
    Department of Communication and Psychology, Aalborg University, Copenhagen, Denmark.
    Koolen, Marijn
    Huygens ING, Royal Netherlands Academy of Arts and Sciences, Netherlands.
    Mobasher, Bamshad
    School of Computing, DePaul University, United States.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Petersen, Casper
    Sampension, Denmark.
    2ndWorkshop on Recommendation in Complex Scenarios (ComplexRec 2018)2018In: RecSys 2018 - 12th ACM Conference on Recommender Systems, Association for Computing Machinery (ACM), 2018, p. 510-511Conference paper (Refereed)
    Abstract [en]

    Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward scenarios. In reality, recommendation is often a more complex problem: it is usually just a single step in the user's more complex background need. These background needs can often place a variety of constraints on which recommendations are interesting to the user and when they are appropriate. However, relatively little research has been done on these complex recommendation scenarios. The ComplexRec 2018 workshop addresses this by providing an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution. © 2018 ACM. 978-1-4503-5901-6/18/10. . . $15.00

  • 4.
    Cremonesi, Paolo
    et al.
    Politecnico di Milano.
    Said, Alan
    Recorded Future.
    Tikk, Domonkos
    GravityR & D.
    Zhou, Michelle
    Juji.
    Introduction to the Special Issue on Recommender System Benchmarking2016In: ACM Transactions on Intelligent Systems and Technology (TIST), ISSN 2157-6904, Vol. 7, no 3, p. 1-4, article id 38Article in journal (Refereed)
  • 5.
    Elsweiler, David
    et al.
    I:IMSK University of Regensburg Germany, Germany.
    Ludwig, Bernd
    I:IMSK University of Regensburg Germany, Germany.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Schäfer, Hanna
    RG: Social Computing TU, Munich, Germany.
    Trattner, Christoph
    Know-Center, Graz, Austria.
    Engendering Health with Recommender Systems: Workshop abstract2016In: RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems, Association for Computing Machinery (ACM), 2016, , p. 2p. 409-410Conference paper (Other academic)
  • 6.
    Elsweiler, David
    et al.
    University of Regensburg, Germany.
    Schäfer, Hanna
    Technical University of Munich, Germany.
    Ludwig, Bernd
    Technical University of Munich, Germany.
    Torkamaan, Helma
    University of Duisburg-Essen, Germany.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Trattner, Christoph
    University of Bergen, Norway.
    Third international workshop on health recommender systems (HealthRecSys 2018)2018In: RecSys 2018 - 12th ACM Conference on Recommender Systems, Association for Computing Machinery (ACM), 2018, p. 517-518Conference paper (Refereed)
    Abstract [en]

    The 3rd International Workshop on Health Recommender Systems was held in conjunction with the 2018 ACM Conference on Recommender Systems in Vancouver, Canada. Following the two prior workshops in 2016 [4] and 2017 [2], the focus of this workshop is to deepen the discussion on health promotion, health care as well as health related methods. This workshop also aims to strengthen the HealthRecSys community, to engage representatives of other health domains into cross-domain collaborations, and to exchange and share infrastructure. 

  • 7.
    Holst, Anders
    et al.
    RISE SICS, Sweden.
    Bouguelia, Mohamed-Rafik
    CAISR, Halmstad, Sweden.
    Görnerup, Olof
    RISE SICS, Sweden.
    Pashami, Sepideh
    CAISR, Halmstad, Sweden.
    Al-Shishtawy, Ahmad
    RISE SICS, Sweden.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Girdzijauskas, Šarunas
    RISE SICS, Sweden.
    Nowaczyk, Sławomir
    CAISR, Halmstad, Sweden.
    Soliman, Amira
    RISE SICS, Sweden.
    Eliciting structure in data2019In: CEUR Workshop Proceedings / [ed] Christoph Trattner, Denis Parra, Nathalie Riche, CEUR-WS , 2019, Vol. 2327Conference paper (Refereed)
    Abstract [en]

    This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. 

  • 8.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    A Short History of the RecSys Challenge2016In: The AI Magazine, ISSN 0738-4602, Vol. 37, no 4, p. 102-104Article in journal (Refereed)
    Abstract [en]

    The RecSys Challenge is a yearly recurring competition focusing on creating the best performing recommendation' approach for a specific scenario. Over the years, the competition has drawn many participants from industry and academia and has become a key part of the ACM Conference on Recommender Systems series. This article presents a brief historical overview of the RecSys Challenge from its inception in 2010 until the seventh iteration in 2016.

  • 9.
    Said, Alan
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Parra, Denis
    Pontifical Catholic University of Chile, Santiago, Chile.
    Pashami, Sepideh
    Halmstad University, Sweden.
    IDM-WSDM 2019: Workshop on interactive data mining2019In: WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery (ACM), 2019, p. 846-847Conference paper (Refereed)
    Abstract [en]

    The first Workshop on Interactive Data Mining is held in Melbourne, Australia, on February 15, 2019 and is co-located with 12th ACM International Conference on Web Search and Data Mining (WSDM 2019). The goal of this workshop is to share and discuss research and projects that focus on interaction with and interactivity of data mining systems. The program includes invited speaker, presentation of research papers, and a discussion session. © 2019 held by the owner/author(s).

  • 10.
    Said, Alan
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bellogín, Alejandro
    Universidad Autónoma de Madrid, Madrid, Spain.
    Coherence and inconsistencies in rating behavior: estimating the magic barrier of recommender systems2018In: User modeling and user-adapted interaction, ISSN 0924-1868, E-ISSN 1573-1391, Vol. 28, no 2, p. 97-125Article in journal (Refereed)
    Abstract [en]

    Recommender Systems have to deal with a wide variety of users and user types that express their preferences in different ways. This difference in user behavior can have a profound impact on the performance of the recommender system. Users receive better (or worse) recommendations depending on the quantity and the quality of the information the system knows about them. Specifically, the inconsistencies in users' preferences impose a lower bound on the error the system may achieve when predicting ratings for one particular user -- this is referred to as the magic barrier.

    In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies -- noise. Furthermore, we propose a measure of the consistency of user ratings (rating coherence) that predicts the performance of recommendation methods. More specifically, we show that user coherence is correlated with the magic barrier; we exploit this correlation to discriminate between easy users (those with a lower magic barrier) and difficult ones (those with a higher magic barrier).We report experiments where the recommendation error for the more coherent users is lower than that of the less coherent ones.We further validate these results by using two public datasets, where the necessary data to identify the magic barrier is not available, in which we obtain similar performance improvements.

  • 11.
    Said, Alan
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Data Science: An Introduction2019In: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 1-6Chapter in book (Refereed)
    Abstract [en]

    This chapter gives a general introduction to data science as a concept and to the topics covered in this book. First, we present a rough definition of data science, and point out how it relates to the areas of statistics, machine learning and big data technologies. Then, we review some of the most relevant tools that can be used in data science ranging from optimization to software. We also discuss the relevance of building models from data. The chapter ends with a detailed review of the structure of the book.

  • 12.
    Schäfer, Hanna
    et al.
    Technical University of Munich, Munich, Germany.
    Elahi, Mehdi
    Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
    Elsweiler, David
    University of Regensburg, Regensburg, Germany.
    Groh, Georg
    Technical University of Munich, Munich, Germany.
    Harvey, Morgan
    Northumbria University, Newcastle, United Kingdom.
    Ludwig, Bernd
    University of Regensburg, Regensburg, Germany.
    Ricci, Francesco
    Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    User Nutrition Modelling and Recommendation - Balancing Simplicity and Complexity2017In: UMAP '17 Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization / [ed] Marko Tkalcic, Dhaval Thakker, Panagiotis Germanakos, Kalina Yacef, Cecile Paris, Olga Santos, Association for Computing Machinery (ACM), 2017, p. 93-96Conference paper (Refereed)
    Abstract [en]

    In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources with respect to nutrition and consider how such issues can be tackled in future systems. We discuss the integration of general nutritional theories into information systems as well as user specific nutritional measures and different approaches to evaluating the utility of a given nutritional model.

  • 13.
    Schäfer, Hanna
    et al.
    Technical University of Munich, Munich, Germany.
    Hors-Fraile, Santiago
    University of Seville, Seville, Spain.
    Karumur, Raghav Pavan
    University of Minnesota, Minneapolis, MN, USA.
    André, Calero Valdez
    RWTH Aachen University, Aachen, Germany.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Torkamaan, Helma
    University of Duisburg-Essen, Duisburg-Essen, Germany.
    Ulmer, Tom
    FHS St. Gallen, St. Gallen, Switzerland.
    Trattner, Christoph
    MODUL University Vienna, Vienna, Austria.
    Towards Health (Aware) Recommender Systems2017In: 2017 ACM Conference on Digital Health (DH'17), London: Association for Computing Machinery (ACM), 2017, p. 157-161Conference paper (Refereed)
    Abstract [en]

    People increasingly use the Internet for obtaining information regarding diseases, diagnoses and available treatments. Currently, many online health portals already provide non-personalized health information in the form of articles. However, it can be challenging to find information relevant to one’s condition, interpret this in context, and understand the medical terms and relationships. Recommender Systems (RS) already help these systems perform precise information filtering. In this short paper, we look one step ahead and show the progress made towards RS helping users find personalized, complex medical interventions or support them with preventive healthcare measures. We identify key challenges that need to be addressed for RS to offer the kind of decision support needed in high-risk domains like healthcare.

  • 14.
    Trattner, Christoph
    et al.
    University of Bergen, Norway.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Boratto, Ludovico
    EURECAT, Spain.
    Felfernig, Alexander
    Graz University of Technology, Austria.
    Evaluating Group Recommender Systems2018In: Group Recommender Systems: An Introduction / [ed] Alexander Felfernig, Ludovico Boratto, Martin Stettinger, Marko Tkalčič, Springer, 2018, p. 59-71Chapter in book (Refereed)
    Abstract [en]

    In the previous chapters, we have learned how to design group recommender systems but did not explicitly discuss how to evaluate them. The evaluation techniques for group recommender systems are often the same or similar to those that are used for single user recommenders. We show how to apply these techniques on the basis of examples and introduce evaluation approaches that are specifically useful in group recommendation scenarios.

  • 15.
    Ventocilla, Elio
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    A Billiard Metaphor for Exploring Complex Graphs2017In: Second Workshop on Supporting Complex Search Tasks / [ed] Marijn Koolen, Jaap Kamps, Toine Bogers, Nick Belkin, Diane Kelly, Emine Yilmaz, 2017, Vol. 1798, p. 37-40Conference paper (Refereed)
    Abstract [en]

    Exploring and revealing relations between the elements is a fre-quent task in exploratory analysis and search. Examples includethat of correlations of attributes in complex data sets, or facetedsearch. Common visual representations for such relations are di-rected graphs or correlation matrices. These types of visual encod-ings are often - if not always - fully constructed before being shownto the user. This can be thought of as a top-down approach, whereusers are presented with a full picture for them to interpret andunderstand. Such a way of presenting data could lead to a visualoverload, specially when it results in complex graphs with highdegrees of nodes and edges. We propose a bottom-up alternativecalled Billiard where few elements are presented at rst and fromwhich a user can interactively construct the rest based on whats/he nds of interest. The concept is based on a billiard metaphorwhere a cue ball (node) has an eect on other elements (associatednodes) when stroke against them.

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