Article
Computer Science, Artificial Intelligence
Fatemeh Rezaimehr, Chitra Dadkhah
Summary: With the increasing amount of data, the use of recommender systems has increased, emphasizing the importance of recommendation quality for users. Most studies focus on collaborative filtering recommender systems, categorizing attack detection methods into clustering, classifying, feature extraction, and probabilistic approaches.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Hongyun Cai, Fuzhi Zhang
Summary: The paper presents an unsupervised approach BS-SC for detecting shilling profiles, which does not require knowledge of attack size or labeling of candidate spammers. By analyzing user behaviors and utilizing behavior features extraction and behavior similarity matrix clustering, BS-SC effectively distinguishes between shilling profiles and genuine profiles. Experimental results show that BS-SC outperforms baseline unsupervised approaches, even when prior knowledge is provided.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Information Systems
Fan Yang, Yong Yue, Gangmin Li, Terry R. Payne, Ka Lok Man
Summary: Researchers propose a knowledge-enhanced user multi-interest modeling for recommender systems (KEMIM) to overcome the sparsity and cold start problem in collaborative filtering. KEMIM utilizes user-item historical interactions and the knowledge graph to expand a user's potential interests. It combines user attribute features with interests and outperforms state-of-the-art baselines in click-through rate prediction and top-K recommendation tasks.
Article
Computer Science, Information Systems
Zheng Huo, Ping He, Lisha Hu, Huanyu Zhao
Summary: In this paper, a differentially private user profile construction method DP-UserPro is proposed, which consists of DP-CLIQUE and privately top-k tags selection. The privacy and utility of DP-UserPro are theoretically analyzed and experimentally evaluated, showing better performance on FNR and precision compared to the Tag Suppression algorithm.
FRONTIERS OF COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Pham Minh Thu Do, Thi Thanh Sang Nguyen
Summary: This paper proposes a novel semantic-enhanced Neural Collaborative Filtering (NCF) model for movie rating prediction and recommendation tasks. By building a semantic knowledge base and user behavior analytic model, combined with user preferences and recommendation model, the proposed model shows better recommendation performance in experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Review
Computer Science, Information Systems
Fethi Fkih
Summary: This paper provides an in-depth review of similarity measures used in collaborative filtering-based recommender systems. Through experimental studies, the performance of different measures is compared, and important conclusions are drawn. Evaluation results show that different similarity measures have different suitability in user-based and item-based recommendations.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Jian Liu, Youling Chen, Qingzhi Liu, Bedir Tekinerdogan
Summary: With the development of cloud manufacturing, recommender systems in CMfg service have become a promising research field. However, the 'Many-to-Many' recommendation mode is increasing due to the time complexity problem. To address this, we propose a similarity-enhanced hybrid group recommendation approach named HGRA for cloud manufacturing. Our approach includes an enhanced user similarity measuring approach, a user subgroup clustering algorithm, and a weighted ranking aggregation model that generates a recommendation list. Experimental results show the feasibility and effectiveness of our approach compared to benchmark solutions, especially in CMfg systems.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Hospitality, Leisure, Sport & Tourism
David Massimo, Francesco Ricci
Summary: Recommender Systems are often evaluated based on their precision in predicting user behavior, but in online settings, precise recommendations may lack novelty. This paper addresses the issue by studying four different RSs that excel on different target criteria such as precision, relevance, and novelty. The study finds that different systems optimize different aspects of recommendation quality, highlighting the importance of balancing precision, relevance, and novelty in RS design.
INFORMATION TECHNOLOGY & TOURISM
(2021)
Article
Computer Science, Information Systems
Soheila Molaei, Amirhossein Havvaei, Hadi Zare, Mahdi Jalili
Summary: The paper proposes a new recommendation approach - Collaborative Deep Forest Learning (CDFL), which aims to improve the performance of recommender systems by learning latent social features and outperforms state-of-the-art CF recommendation methods based on experiments with real-world datasets from different domains.
Article
Computer Science, Information Systems
Pablo Perez-Nunez, Jorge Diez, Oscar Luaces, Antonio Bahamonde
Summary: Recommender systems are valuable tools for companies to understand customer preferences and offer personalized marketing campaigns. Clustering tools are key in detecting groups of customers with similar tastes.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Business
Negin Ghasemi, Saeedeh Momtazi
Summary: The paper presents a model to enhance recommender systems by finding similar users based on their reviews in addition to their ratings. Experimental results show that the model based on Long Short Term Memory (LSTM) network achieves the best performance.
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
R. J. Kuo, Zhen Wu
Summary: With the rise of the Internet service industry, recommender systems have been widely used. This study proposes a recommendation algorithm based on evolutionary algorithm, combining user characteristic clustering and matrix factorization, to improve recommendation quality.
JOURNAL OF INTERNET TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Rosmamalmi Mat Nawi, Shahrul Azman Mohd Noah, Lailatul Qadri Zakaria
Summary: By using LOD technology to address data sparsity issues beforehand, a GRS-LOD model was proposed. Experimental results indicated that the model, which utilized the Average strategy and individual profile aggregation approach, outperformed baseline studies in terms of prediction accuracy and relevancy.
Article
Computer Science, Artificial Intelligence
Peng Zhou, Xia Wang, Liang Du
Summary: Unsupervised feature selection is an important task in machine learning but suffers from stability and robustness issues due to the absence of labels. This paper proposes a novel bi-level feature selection ensemble method that not only ensembles at the feature level but also learns a consensus clustering result to guide the feature selection, outperforming other state-of-the-art methods.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
F. Ortega, J. Mayor, D. Lopez-Fernandez, R. Lara-Cabrera
Summary: CF4J 2.0 is a framework designed for research experiments based on collaborative filtering, with features like implemented algorithms, quality measures, parallel execution, and abstract classes for developers to customize. The new version focuses on simple deployment, reproducible science, hyper-parameter optimization, data analysis, and community openness as an open-source project.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Veronika Peralta, Patrick Marcel, Willeme Verdeaux, Aboubakar Sidikhy Diakhaby
INFORMATION SYSTEMS
(2020)
Article
Multidisciplinary Sciences
Franck Boizard, Benedicte Buffin-Meyer, Julien Aligon, Olivier Teste, Joost P. Schanstra, Julie Klein
Summary: PRYNT, a new prioritization strategy based on urinary proteome data, outperformed other available methods in prioritizing kidney disease candidates. It can indirectly predict key proteins and has the potential for application in other biofluids and diseases.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Information Systems
Matteo Francia, Patrick Marcel, Veronika Peralta, Stefano Rizzi
Summary: IAM is a new model paradigm that allows users to explore data by expressing analysis intentions and returns annotated multidimensional data. Research challenges include automatically tuning model size, estimating model component interestingness, selecting effective visualizations, and designing visual metaphors for interaction. Method effectiveness is evaluated based on user effort, efficiency, and scalability.
INFORMATION SYSTEMS FRONTIERS
(2022)
Article
Computer Science, Information Systems
Gabriel Ferrettini, Elodie Escriva, Julien Aligon, Jean-Baptiste Excoffier, Chantal Soule-Dupuy
Summary: With the increasing application of machine learning in various fields, there is a growing demand for understanding the internal operations of models. This paper proposes a method based on attribute coalition detection, which proves to be more efficient than existing methods, reducing computation time while maintaining acceptable accuracy.
INFORMATION SYSTEMS FRONTIERS
(2022)
Article
Health Care Sciences & Services
Paul Monsarrat, David Bernard, Mathieu Marty, Chiara Cecchin-Albertoni, Emmanuel Doumard, Laure Gez, Julien Aligon, Jean-Noel Vergnes, Louis Casteilla, Philippe Kemoun
Summary: This study proposes a personalized explainable machine learning algorithm for identifying individuals at risk of developing periodontal diseases. By analyzing the data of 532 subjects, the most contributive variables for periodontal health prediction were found to be age, BMI, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status. The algorithm clearly shows different risk profiles before and after a certain age, providing new strategies for periodontal health prediction.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Computer Science, Information Systems
Faten El Outa, Patrick Marcel, Veronika Peralta, Panos Vassiliadis
Summary: Data narration is the process of crafting narratives using facts extracted from data exploration and analysis, presented through interactive visualizations. This article introduces a comprehensive and well-founded process that supports the entire cycle of data narration, from exploring the data to visualizing the narrative. It also covers a wide range of crafting practices and is based on a conceptual model of the domain.
INFORMATION SYSTEMS FRONTIERS
(2023)
Article
Computer Science, Information Systems
Haomiao Wang, Emmanuel Doumard, Chantal Soule-Dupuy, Philippe Kemoun, Julien Aligon, Paul Monsarrat
Summary: With the increasing use of Machine Learning in the biomedical field, there is a growing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex relationships between variables for medical practitioners. Feature Selection (FS) is commonly used to reduce the number of variables while preserving information, but there is limited research on the relationship between FS and model explanations. This study demonstrates the complementary nature of explanation-based metrics, accuracy, and retention rate in selecting the most appropriate FS/ML models, providing a framework for offering healthcare professionals the appropriate FS technique based on their preferences.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Faten El Outa, Patrick Marcel, Veronika Peralta, Raphael da Silva, Marie Chagnoux, Panos Vassiliadis
Summary: This article proposes a comprehensive and well-founded process for crafting data narratives, covering the complete cycle from data exploration to visual rendering, and based on a conceptual model.
ADVANCES IN DATABASES AND INFORMATION SYSTEMS, ADBIS 2022
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Nicolas Ringuet, Patrick Marcel, Nicolas Labroche, Thomas Devogele, Christophe Bortolaso
Summary: While personal coach applications are widely used, personal lifelong pathway co-construction is often overlooked. This paper presents a generic model that supports interaction between advisees and advisors and includes key aspects of lifelong pathways. The importance of this model is illustrated through its application in a guidance system.
CONCEPTUAL MODELING (ER 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Flavia Serra, Veronika Peralta, Adriana Marotta, Patrick Marcel
Summary: This paper highlights the importance of context for data quality and proposes a context model specifically designed for data quality management.
CONCEPTUAL MODELING (ER 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Alexandre Chanson, Thomas Devogele, Nicolas Labroche, Patrick Marcel, Nicolas Ringuet, Vincent T'Kindt
Summary: This work addresses the problem of recommending lifelong pathways, modeling them as particular chain composite items and formalizing the recommendation problem as an orienteering problem. The approach is experimented with artificial and real datasets, showing promising results as a building block for an interactive lifelong pathways recommender system.
BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY (DAWAK 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yan Zhao, Julien Aligon, Gabriel Ferrettini, Imen Megdiche, Franck Ravat, Chantal Soule-Dupuy
Summary: The aim of this paper is to establish an easily accessible, reusable data lake by proposing an analysis-oriented metadata model. This model includes descriptive information of datasets and attributes, as well as all metadata related to machine learning analyzes on these datasets. The implementation of a data lake metadata management application allows users to search for and use existing data, processes, and analyses by finding relevant metadata stored in a NoSQL data store within the data lake.
IDEAS 2021: 25TH INTERNATIONAL DATABASE ENGINEERING & APPLICATIONS SYMPOSIUM
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Gabriel Ferrettini, Julien Aligon, Chantal Soule-Dupuy
SOFSEM 2020: THEORY AND PRACTICE OF COMPUTER SCIENCE
(2020)
Article
Computer Science, Information Systems
Alessio Cecconi, Luca Barbaro, Claudio Di Ciccio, Arik Senderovich
Summary: This paper introduces a framework for designing probabilistic measures for declarative process specifications, which can assess the degree of compliance between process data and specifications. Through experiments, the applicability of the approach for various process mining tasks is demonstrated.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Mahei Manhai Li, Philipp Reinhard, Christoph Peters, Sarah Oeste-Reiss, Jan Marco Leimeister
Summary: This article introduces a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures, and the evaluation shows that recommendations after label improvement have increased user ratings.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Anton Yeshchenko, Jan Mendling
Summary: This paper presents the development of event sequence data analysis techniques in different fields and proposes an integrated framework to facilitate collaboration and research synergy across various domains.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Iris Reinhartz-Berger, Alan Hartman, Doron Kliger
Summary: Many IT departments provide solutions that partially meet the needs of business units. This research aims to develop a data-driven analysis method to support the selection of solutions with higher prospects of adoption and identify design gaps and barriers.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Orlenys Lopez-Pintado, Marlon Dumas, Jonas Berx
Summary: Business process simulation is a versatile technique that predicts the impact of changes on process performance. However, previous approaches have limitations due to their treatment of resources as undifferentiated entities. This article addresses this issue by proposing a new simulation approach that treats each resource as an individual entity with its own performance and availability. The article also presents methods for discovering simulation models with differentiated resources and optimizing resource availability calendars. Empirical evaluation demonstrates that differentiated resource models better replicate cycle time distributions and work rhythm, and iterative optimization of resource allocations and calendars leads to improved cost-time tradeoffs.
INFORMATION SYSTEMS
(2024)