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Sökning: hsv:(NATURVETENSKAP) hsv:(Data och informationsvetenskap) hsv:(Annan data och informationsvetenskap) > Said Alan

  • Resultat 1-10 av 13
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1.
  • Ferwerda, Bruce, 1986-, et al. (författare)
  • Reality Check – Conducting Real World Studies
  • 2023
  • Ingår i: Frontiers of Information Access Experimentation for Research and Education : Report from Dagstuhl Seminar 23031. - Wadern : Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. - 2192-5283. ; , s. 20-40
  • Bokkapitel (refereegranskat)abstract
    • Information retrieval and recommender systems are deployed in real world environments. Therefore, to get a real feeling for the system, we should study their characteristics in “real world studies”. This raises the question: What does it mean for a study to be realistic? Does it mean the user has to be a real user of the system or can anyone participate in a study of the system? Does it mean the system needs to be perceived as realistic by the user? Does it mean the manipulations need to be perceived as realistic by the user?
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2.
  • Said, Alan, et al. (författare)
  • Data Science : An Introduction
  • 2019
  • Ingår i: Data Science in Practice. - Cham : Springer. - 9783319975566 - 9783319975559 ; , s. 1-6
  • Bokkapitel (refereegranskat)abstract
    • 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.
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3.
  • Saxborn, Maria, et al. (författare)
  • Trust Through Recommendation in E-commerce
  • 2024
  • Ingår i: CHIIR '24: Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval. - New York : ACM.
  • Konferensbidrag (refereegranskat)abstract
    • We explore the influence of recommender systems on trust among consumers in the fashion e-commerce domain. Anchoring on the Trust Building Model (TBM), we investigate its adaptability and applicability in the context of interactive communication in recommender systems. Primarily leaning on qualitative data collection methods, namely semi-structured interviews, our work evaluates the classic TBM components – structure assurance, perceived reputation, perceived site quality, perceived web risk, trusting belief, and behavioral intention – affirming their relevance to recommender systems. Furthermore, new components, i.e., perceived service and recommendation quality, previous experience, perceived enjoyment, perceived recommendation authenticity, and intention to share interaction data, were examined in the context of recommender systems. Significantly, our study unveils that trusting beliefs can notably influence TBM’s preliminary behavioral intentions, with the competence belief having the most substantial impact, challenging the conventional TBM findings. The outcomes highlight that consumers place heightened value on the tangible provisions from the company over ethics-based factors like integrity. The proposed refined TBM offers potential in enhancing recommender systems in fashion e-commerce, facilitating a better understanding of consumer behavior and trust dynamics.
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4.
  • Bellogín, Alejandro, et al. (författare)
  • Improving accountability in recommender systems research through reproducibility
  • 2021
  • Ingår i: User Modeling and User-Adapted Interaction. - : Springer Science and Business Media LLC. - 0924-1868 .- 1573-1391. ; 31
  • Tidskriftsartikel (refereegranskat)abstract
    • Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works. These issues have become increasingly prevalent in recent literature. Reasons for this include societal movements around intelligent systems and artificial intelligence striving toward fair and objective use of human behavioral data (as in Machine Learning, Information Retrieval, or Human–Computer Interaction). Society has grown to expect explanations and transparency standards regarding the underlying algorithms making automated decisions for and around us. This work surveys existing definitions of these concepts and proposes a coherent terminology for recommender systems research, with the goal to connect reproducibility to accountability. We achieve this by introducing several guidelines and steps that lead to reproducible and, hence, accountable experimental workflows and research. We additionally analyze several instantiations of recommender system implementations available in the literature and discuss the extent to which they fit in the introduced framework. With this work, we aim to shed light on this important problem and facilitate progress in the field by increasing the accountability of research.
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5.
  • Said, Alan, et al. (författare)
  • IDM-WSDM 2019 : Workshop on interactive data mining
  • 2019
  • Ingår i: WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450359405 ; , s. 846-847
  • Konferensbidrag (refereegranskat)abstract
    • 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).
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6.
  • Bellogín, Alejandro, et al. (författare)
  • Recommender Systems Evaluation
  • 2018. - 2
  • Ingår i: Encyclopedia of Social Network Analysis and Mining. - New York, NY : Springer. - 9781493971305 - 9781493971312 - 9781493971329
  • Bokkapitel (refereegranskat)
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7.
  • Edkrantz, Michel, et al. (författare)
  • Predicting cyber vulnerability exploits with machine learning
  • 2015
  • Ingår i: Frontiers in Artificial Intelligence and Applications. - 0922-6389.
  • Konferensbidrag (refereegranskat)abstract
    • For an information security manager it can be a daunting task to keep up and assess which new cyber vulnerabilities to prioritize patching first. Every day numerous new vulnerabilities and exploits are reported for a wide variety of different software configurations. We use machine learning to make automatic predictions for unseen vulnerabilities based on previous exploit patterns. As sources for historic vulnerability data, we use the National Vulnerability Database (NVD) and the Exploit Database (EDB). Our work shows that common words from the vulnerability descriptions, external references, and vendor products, are the most important features to consider. Common Vulnerability Scoring System (CVSS) scores and categorical parameters, and Common Weakness Enumeration (CWE) numbers are redundant when a large number of common words are used, since this information is often contained within the vulnerability description. Using machine learning algorithms, it is possible to get a prediction accuracy of 83% for binary classification. In comparison, the performance differences between some of the algorithms are marginal with respect to metrics such as accuracy, precision, and recall. The best classifier with respect to both performance metrics and execution time is a linear time Support Vector Machine (SVM) algorithm. We conclude that in order to get better predictions the data quality must be enhanced.
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8.
  • Hauptmann, Hanna, et al. (författare)
  • Research directions in recommender systems for health and well-being: A Preface to the Special Issue
  • 2022
  • Ingår i: User Modeling and User-Adapted Interaction. - : Springer Science and Business Media LLC. - 0924-1868 .- 1573-1391. ; 32:5, s. 781 - 786
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • Recommender systems have been put to use in the entertainment and e-commerce domains for decades, and in these decades, recommender systems have grown and matured into reliable and ubiquitous systems in today’s digital landscape. Relying on this maturity, the application of recommender systems for health and well-being has seen a rise in recent years, paving the way for tailored and personalized systems that support caretakers, caregivers, and other users in the health domain. In this introduction, we give a brief overview of the stakes, the requirements, and the possibilities that recommender systems for health and well-being bring.
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9.
  • Said, Alan, et al. (författare)
  • Coherence and inconsistencies in rating behavior : estimating the magic barrier of recommender systems
  • 2018
  • Ingår i: User modeling and user-adapted interaction. - : Springer. - 0924-1868 .- 1573-1391. ; 28:2, s. 97-125
  • Tidskriftsartikel (refereegranskat)abstract
    • 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.
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10.
  • Said, Alan, et al. (författare)
  • Leveraging Large Language Models for Goal-driven Interactive Recommendations
  • 2023
  • Ingår i: HAI '23: Proceedings of the 11th International Conference on Human-Agent Interaction.
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • We present a proof of concept application for interactive recommendations and explanations leveraging the capabilities of Large Language Models (LLMs). The application creates a highly interactive user-driven setting for recommendations giving users the possibility to explicitly tailor recommendations to their needs. Using the possibilities brought by LLMs, the application further generates convincing explanations of recommendations, aligned with the explicitly stated goals of the users. The web application continuously improves by incorporating user feedback and updating recommendations and explanations as needed.
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  • Resultat 1-10 av 13

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