smart.lab.tunisia@gmail.com +216 71 588 443
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Jihene LATRECH

Informations générales

Jihene LATRECH
Grade

PES

Biographie courte

Jihene Latrech is a member of the Strategies for Modelling and Artificial Intelligence Laboratory (SMART-Lab). Her research focuses primarily on recommender systems, leveraging machine learning and deep learning techniques.

Since September 2017, she has been serving as a professor of the common core curriculum at the Higher Institute of Information and Communication Technologies (ISTIC), a public academic institution under the University of Carthage, located in the Borj Cédria Technopole. From September 2005 to June 2012, she was a professor at the Faculty of Science of Gafsa (FSG), under the University of Gafsa.

Jihene holds a Bachelor’s degree in Computer Science Applied to Management from the Higher Institute of Management of Tunis (ISG Tunis), University of Tunis (2004). She earned a Research Master’s degree in Decision Computing Sciences and Techniques (STID), specializing in Intelligent Methods for Decision Support (MIAD), from ISG Tunis in 2021. In 2025, she obtained her PhD in Computer Science Applied to Management from the same institution.

 

Axes de recherche

Recommendation systems
Artificial Intelligence
Machine Learning

Publications

  • 2025
    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    CoD-MaF: toward a Context-Driven Collaborative Filtering using Contextual Biased Matrix Factorization

    International Journal of Data Science and Analytics, 1-18., 2025

    Résumé

    Contextual recommendation has become attainable through the massive amounts of contextual information generated by smartphones and Internet of Things (IoT) devices. The availability of a huge amount of contextual data paves the way for a revolution in recommendation systems. It overcomes the static nature of personalization, which does not allow the discovery of new items and interests, toward a contextualization of the user’s tastes, which are in constant evolution. In this paper, we proposed CoD-MaF, a Context-Driven Collaborative Filtering using Contextual biased Matrix Factorization. Our approach employs feature selection methods to extract the most influential contextual features that will be used to cluster the users using K-means algorithm. The model then performs a collaborative filtering based on matrix factorization with improved contextual biases to suggest relevant personalized recommendations. We highlighted the performance of our method through experiments on four datasets (LDOS-CoMoDa, STS-Travel, IncarMusic and Frappe). Our model enhanced the accuracy of predictions and achieved competitive performance compared to baseline methods in metrics of RMSE and MAE.

    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    Machine Learning Based Collaborative Filtering Using Jensen-Shannon Divergence for Context-Driven Recommendations

    *, 2025

    Résumé

    This research presents a machine learning-based context-driven collaborative filtering approach with three
    steps: contextual clustering, weighted similarity assessment, and collaborative filtering. User data is clustered
    across 3 aspects, and similarity scores are calculated, dynamically weighted, and aggregated into a normalized
    User-User similarity matrix. Collaborative filtering is then applied to generate contextual recommendations.
    Experiments on the LDOS-CoMoDa dataset demonstrated good performance, with RMSE and MAE rates of
    0.5774 and 0.3333 respectively, outperforming reference approaches.

  • Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    CoDFi-DL: a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning.

    Journal of Supercomputing, 80(1), 2024

    Résumé

    The cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or items. Addressing the challenge of providing personalized recommendations in the cold start scenario is crucial. This research proposes a new hybrid recommender system named CoDFi-DL which combines demographic and enhanced collaborative filtering. The demographic filtering is performed through a deep neural network (DNN) and used to solve the new user cold start problem. The enhanced collaborative filtering component of our model focuses on delivering personalized recommendations through a neighborhood-based method. The major contribution in this research is the DNN-based demographic filtering which overcomes the new user cold start problem and enhances the collaborative filtering process. Our system significantly improves the relevancy of the recommendation task and thus provides personalized recommended items to cold users. To evaluate the effectiveness of our approach, we conducted experiments on real multi-label datasets, 1M and 100K MovieLens. CoDFi-DL recommender system showed higher performance in comparison with baseline methods, achieving lower RMSE rates of 0.5710 on the 1M MovieLens dataset and 0.6127 on the 100K MovieLens dataset.

    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    Twit-CoFiD: a hybrid recommender system based on tweet sentiment analysis

    Social Network Analysis and Mining, 14(1), 123., 2024

    Résumé

    Internet users are overwhelmed by the vast number of services and products to choose from. This data deluge has led to the need for recommender systems. Simultaneously, the explosion of interactions on social networks is constantly increasing. These interactions produce a large amount of content that incites organizations and individuals to exploit it as a support for decision making. In our research, we propose, Twit-CoFiD, a hybrid recommender system based on tweet sentiment analysis which performs a demographic filtering to use its outputs in an enhanced collaborative filtering enriched with a sentiment analysis component. The demographic filtering, based on a Deep Neural Network (DNN), allows to overcome the cold start problem. The sentiment analysis of Twitter data combined with the enhanced collaborative filtering makes recommendations more relevant and personalized. Experiments were conducted on 1M and 100K Movielens datasets. Our system was compared to other existing methods in terms of predictive accuracy, assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. It yielded improved results, achieving lower RMSE and MAE rates of 0.4474 and 0.3186 on 100K Movielens dataset and of 0.3609 and 0.3315 on 1M Movielens dataset.

    Jihene LATRECH, Zahra Kodia, Nadia Ben Azzouna

    Context-based Collaborative Filtering: K-Means Clustering and Contextual Matrix Factorization*

    In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1-5). IEEE., 2024

    Résumé

    The rapid expansion of contextual information from smartphones and Internet of Things (IoT) devices paved the way for Context-Aware Recommendation Systems (CARS). This abundance of contextual data heralds a transformative era for traditional recommendation systems. In alignment with this trend, we propose a novel model which provides personalized recommendations based on context. Our approach uses K-means algorithm to cluster users based on contextual features. Then, the model performs collaborative filtering based on matrix factorization with enhanced contextual biases to provide relevant recommendations. We demonstrated the performance of our method through experiments conducted on the movie recommender dataset LDOS-CoMoDa. The experimental results showed the effective performance of our proposal compared to reference methods, achieving an RMSE of 0.7416 and an MAE of 0.6183.