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- short-paperJune 2024
Beyond Trade-offs: Unveiling Fairness-Constrained Diversity in News Recommender Systems
UMAP '24: Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and PersonalizationJune 2024, Pages 143–148https://doi.org/10.1145/3627043.3659571Recommender Systems have played an important role in our daily lives for many years. However, it is only recently that their social impact has raised ethical issues and has thus been considered in the design of such systems. Particularly, News ...
- ArticleMay 2024
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
AbstractKnowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some tasks, a few ...
- ArticleMay 2024
Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction
AbstractKnowledge graph embedding models (KGEMs) are used for various tasks related to knowledge graphs (KGs), including link prediction. They are trained with loss functions that consider batches of true and false triples. However, different kinds of ...
- ArticleMay 2024
Do Similar Entities Have Similar Embeddings?
AbstractKnowledge graph embedding models (KGEMs) developed for link prediction learn vector representations for entities in a knowledge graph, known as embeddings. A common tacit assumption is the KGE entity similarity assumption, which states that these ...
- research-articleMay 2024
All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media
SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied ComputingApril 2024, Pages 1469–1478https://doi.org/10.1145/3605098.3635972Online polarization has attracted the attention of researchers for many years. Its effects on society are a cause for concern, and the design of personalized depolarization strategies appears to be a key solution. Such strategies should rely on a fine ...
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- research-articleDecember 2023
Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
K-CAP '23: Proceedings of the 12th Knowledge Capture Conference 2023December 2023, Pages 188–196https://doi.org/10.1145/3587259.3627550Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). Learning versatile KGEs is ...
- extended-abstractJune 2023
A Multi-factorial Analysis of Polarization on Social Media
UMAP '23 Adjunct: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and PersonalizationJune 2023, Pages 1–6https://doi.org/10.1145/3563359.3597393Polarization is an increasingly worrying phenomenon within social media. Recent work has made it possible to detect and even quantify polarization. Nevertheless, the few existing metrics, although defined in a continuous space, often lead to a unimodal ...
- research-articleDecember 2022
DEEP, a methodology for entity extraction using organizational patterns: Application to job offers
Knowledge-Based Systems (KNBS), Volume 258, Issue CDec 2022https://doi.org/10.1016/j.knosys.2022.109573AbstractPlain texts written in natural language may have several specific features, such as organizational patterns and an ambiguous and evolving vocabulary. From the literature, entity extraction approaches are not sufficient to consider ...
Highlights- Improving entity extraction in plain text documents using sequence labelling.
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- ArticleSeptember 2022
New Strategies for Learning Knowledge Graph Embeddings: The Recommendation Case
Knowledge Engineering and Knowledge ManagementSep 2022, Pages 66–80https://doi.org/10.1007/978-3-031-17105-5_5AbstractKnowledge graph embedding models encode elements of a graph into a low-dimensional space that supports several downstream tasks. This work is concerned with the recommendation task, which we approach as a link prediction task on a single target ...
- ArticleJuly 2022
Managing Learners’ Memory Strength in a POMDP-Based Learning Path Recommender System
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral ConsortiumJul 2022, Pages 284–288https://doi.org/10.1007/978-3-031-11647-6_53AbstractThis paper views the learning path recommendation task as a sequential decision problem and considers Partially Observable Markov Decision Process (POMDP) as an adequate approach. This work proposes M-POMDP, a POMDP-based recommendation model that ...
- posterJuly 2022
Independent influence of exploration and exploitation for metaheuristic-based recommendations
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022, Pages 475–478https://doi.org/10.1145/3520304.3528972Exploration and exploitation (E&E) of a search space are two fundamental processes in many fields of artificial intelligence. Indeed, when the search space is vast, it is important to ensure that many of its regions are examined, so as not to get trapped ...
- research-articleJuly 2022
Being Diverse is Not Enough: Rethinking Diversity Evaluation to Meet Challenges of News Recommender Systems
UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and PersonalizationJuly 2022, Pages 222–233https://doi.org/10.1145/3511047.3538030Modern societies face many challenges, one of them is the rise of affective polarization over the last 4 decades. In an attempt to understand its reasons, many researchers have questioned the role of Social Media in general, and Recommender Systems (RS)...
- research-articleJanuary 2022
Presence Variations for E&E Transitions Explainability
Procedia Computer Science (PROCS), Volume 207, Issue C2022, Pages 1802–1811https://doi.org/10.1016/j.procs.2022.09.238AbstractThe exploration and exploitation (E&E) of a search space are fundamental processes in many fields of artificial intelligence. Indeed, it is important to ensure that many regions of the search space are examined, so as not to get trapped in a local ...
- ArticleSeptember 2020
New Measures for Offline Evaluation of Learning Path Recommenders
Addressing Global Challenges and Quality EducationSep 2020, Pages 259–273https://doi.org/10.1007/978-3-030-57717-9_19AbstractRecommending students useful and effective learning paths is highly valuable to improve their learning experience. The evaluation of the effectiveness of this recommendation is a challenging task that can be performed online or offline. Online ...
- research-articleOctober 2019
C3Ro: An efficient mining algorithm of extended-closed contiguous robust sequential patterns in noisy data
Expert Systems with Applications: An International Journal (EXWA), Volume 131, Issue COct 2019, Pages 172–189https://doi.org/10.1016/j.eswa.2019.04.058Highlights- New notion of apprehensibility to represent the quality of pattern mining output.
Sequential pattern mining has been the focus of many works, but still faces a tough challenge in the mining of large databases for both efficiency and apprehensibility of its resulting set. To overcome these issues, the most promising ...
- research-articleMarch 2018
DEER
Expert Systems with Applications: An International Journal (EXWA), Volume 93, Issue CMarch 2018, Pages 283–298https://doi.org/10.1016/j.eswa.2017.10.035An algorithm of episode rules mining in an event sequence for early prediction.The extracted rules have a temporally distant consequent and a minimal antecedent.The algorithm outperforms traditional algorithms in running time and scalability. Events ...
- articleApril 2017
Identifying representative users in matrix factorization-based recommender systems: application to solving the content-less new item cold-start problem
Journal of Intelligent Information Systems (JIIS), Volume 48, Issue 2April 2017, Pages 365–397https://doi.org/10.1007/s10844-016-0418-3Matrix factorization has proven to be one of the most accurate recommendation approaches. However, it faces one major shortcoming: the latent features that result from the factorization are not directly interpretable. Providing interpretation for these ...
- ArticleAugust 2016
Sets of contrasting rules to identify trigger factors
ECAI'16: Proceedings of the Twenty-second European Conference on Artificial IntelligenceAugust 2016, Pages 1728–1729https://doi.org/10.3233/978-1-61499-672-9-1728In this paper we introduce a new pattern, referred to as "set of contrasting rules". The main originality of this pattern is that it allows to easily identify trigger factors: factors that can bring some event state changes. In real applications this ...
- research-articleJuly 2016
Identifying Grey Sheep Users in Collaborative Filtering: A Distribution-Based Technique
UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and PersonalizationJuly 2016, Pages 17–26https://doi.org/10.1145/2930238.2930242The collaborative filtering (CF) approach in recommender systems assumes that users' preferences are consistent among users. Although accurate, this approach fails on some users. We presume that some of these users belong to a small community of users ...
- ArticleNovember 2015
Predict the Emergence: Application to Competencies in Job Offers
ICTAI '15: Proceedings of the 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)November 2015, Pages 612–619https://doi.org/10.1109/ICTAI.2015.94Predicting the emergence of an event enables to anticipate and make decisions upstream. For instance, in the employment sector, it becomes necessary to anticipate the emergence of competencies requirements to help job seekers, education and training ...