In today’s digital landscape, recommender systems assist users in navigating the vast amount of available data. Within the realm of information access, a subset of such systems called News Recommender Systems help users find news content that interests them. However, by prioritizing traditional accuracy-focused optimization, these systems contribute to the formation of filter bubbles, restricting users’ exposure to diverse viewpoints and exacerbating polarization. To address this issue, beyond-accuracy factors like diversity have been integrated into recommendation. Yet, such approaches can be ineffective and even unintentionally influence user opinions. This raises major ethical concerns as systems lack the legitimacy to shape opinions. This article presents the ADF framework, a novel approach designed to optimize accuracy, diversity, and fairness simultaneously. Unlike conventional models that manage fairness in a tradeoff, ADF establishes fairness as a core constraint. The framework relies on an innovative fairness-constrained diversification strategy, ensuring that users are exposed to a broader range of opinions, without being oriented toward specific viewpoints. ADF is adaptable to various diversity metrics and provides personalized diversification, independent of the underlying recommendation algorithms. Through real-world benchmark datasets and multiple recommendation models, experimental evaluation confirmed that ADF limits the impact on accuracy, enhances diversity, and crucially upholds fairness.

These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identifies patterns and temporal influences with low execution time, showcasing its potential in financial system incident prediction.

Knowledge 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 desirable as it makes them useful for a broad range of tasks. However, KGEMs are usually trained for a specific task, which makes their embeddings task-dependent. In parallel, the widespread assumption that KGEMs actually create a semantic representation of the underlying entities and relations (e.g., project similar entities closer than dissimilar ones) has been challenged. In this work, we design heuristics for generating protographs – small, modified versions of a KG that leverage RDF/S information. The learnt protograph-based embeddings are meant to encapsulate the semantics of a KG, and can be leveraged in learning KGEs that, in turn, also better capture semantics. Extensive experiments on various evaluation benchmarks demonstrate the soundness of this approach, which we call Modular and Agnostic SCHema-based Integration of protograph Embeddings (MASCHInE). In particular, MASCHInE helps produce more versatile KGEs that yield substantially better performance for entity clustering and node classification tasks. For link prediction, using MASCHinE substantially increases the number of semantically valid predictions with equivalent rank-based performance.

More and more personalization systems are emerging to reduce the information overload of the Web. As a result, it has become vital to model users' preferences accurately. Our focus lies in the quality of users' expressed preferences, in terms of reliability and stability through time. Today, users are often brought to express their preferences through ratings on a multi-point scale. However, several studies have highlighted problems with ratings. We propose a new preference modality whereby users compare items two-by two ("I prefer x to y").This initial work on comparisons shows that users are in favor of this new preference mechanism and that comparisons are almost 20% more stable over time than those conveyed through ratings, thus more reliable. These encouraging findings let us think that comparisons may lead to a better user modeling and an increase in the quality of personalization services, such as recommender systems.

This paper proposes a new approach of mentor selection in memory-based collaborative filtering when no rating is available. Users are represented under the form of a social network. The selection of mentors is performed through the use of a community detection algorithm used in the frame of social networks. It allows to recommend items to a given user, by applying democratic voting rules within his community.

The development of the Web engendered the emergence of virtual communities. Analyzing information flows and discovering leaders through these communities becomes thus, a major challenge in different application areas. In this paper, we present an algorithm that aims at detecting leaders in the context of behavioral networks. This algorithm considers the high connectivity and the potentiality of propagating accurate appreciations so as to detect reliable leaders through these networks. This approach is evaluated in terms of precision using a real usage dataset. The results of the experimentation show the interest of our approach to detect TopN behavioral leaders that predict accurately the preferences of the other users. Besides, our approach can be harnessed in different application areas caring about the role of leaders.

Recommender systems are widely used for personalization of information on the web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique. However, classical CF systems use only direct links and common features to model relationships between users. This paper presents a new Collaborative Filtering approach (BNCF) based on a behavioral network that uses navigational patterns to model relationships between users and exploits social networks techniques, such as transitivity, to explore additional links throughout the behavioral network. The final aim consists in involving these new links in prediction generation, to improve recommendations quality. BNCF is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions. Indeed, BNCF highly improves the accuracy of predictions, especially in terms of HMAE.

Recommender systems filter resources for a given user by predicting the most pertinent resource given a specific context. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream. The underlying hypothesis is that the resources order in the stream results from the intrinsic logic of the user's behavior. The sequence based recommender we propose is inspired from language modeling and integrates skipping techniques. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous sequences to compute recommendations enhances the accuracy. Moreover, we propose a skipping variant that provides a high accuracy while being less complex.

This work presents several statistical methods for topic identification on two kinds of textual data: newspaper articles and e-mails. Five methods are tested on these two corpora: topic unigrams, cache model, TFIDF classijier, topic peqdexity, and weighted model. Our work aims to study these methods by confronting them to very diferent data. This study is very fruitful for our research. Statistical topic identiJication methods depend not only on a corpus, but also on its type. One of the methods achieves a topic identiJcation of 80% on a general newspaper corpus but does not exceed 30% on e-mail corpus. Another method gives the best result on e-mails, but has not the same behavior on a newspaper corpus. We also show in this paper that almost all our methods achieve good results in retrieving the first two manually annotated labels.

We present several methods for topic detection on newspaper articles, using either a general vocabulary or topic-specific vocabularies. Specific vocabularies are determined manually or statistically. In both cases, we aim at finding the most representative words of a topic. Several methods have been experimented, the first one is based on perplexity, this method achieves a 100% topic identification rate, on large test corpora, when the two first propositions are taken into account. Other methods are based on statistical counts and achieve 94% of identification on smaller test corpora. The major challenge of this work is to identify topics with only few words in order to be able, during speech recognition, to determine the best adequate language model.