@conference {Lahlou201864,
	title = {Textual context aware factorization machines: Improving recommendation by leveraging users{\textquoteright} reviews},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	pages = {64-69},
	doi = {10.1145/3289100.3289111},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058629650\&doi=10.1145\%2f3289100.3289111\&partnerID=40\&md5=c2869c20ec04d08f675efb27e1536863},
	author = {Lahlou, F.Z. and Benbrahim, H. and Kassou, I.}
}
@conference {Oufkir20162744,
	title = {Towards a reference model for Knowledge Management performance measurement},
	booktitle = {Proceedings of the 27th International Business Information Management Association Conference - Innovation Management and Education Excellence Vision 2020: From Regional Development Sustainability to Global Economic Growth, IBIMA 2016},
	year = {2016},
	note = {cited By 0},
	pages = {2744-2755},
	abstract = {Knowledge Management (KM) projects are socio-technical systems that enable knowledge activities and ensure that the right knowledge gets to the right person at the right time. The growing number of KM applications confirms the need for a performance measurement model able to assess the diversity of KM initiatives in order to rationalize their usage. To the best of our knowledge, a generic KM model for performance measurement fitting any kind of KM projects is missing in the literature. Instead, existing approaches are specific to a particular KM project or assess the overall KM of the organization. In this paper, we propose a KM model for performance measurement that is built on existing KM models literature and enhanced with theoretical findings. It also follows the reference modelling design process in view of producing a high quality model. Copyright {\textcopyright} 2016 International Business Information Management Association},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984611689\&partnerID=40\&md5=6ee42ddfe7a2a3ce0b1f2f99f09aa05b},
	author = {Oufkir, L. and Fredj, M. and Kassou, I.}
}
@conference {Lahlou2013,
	title = {A Text Classification based method for context extraction from online reviews},
	booktitle = {2013 8th International Conference on Intelligent Systems: Theories and Applications, SITA 2013},
	year = {2013},
	note = {cited By 1},
	abstract = {Recommender systems are systems that filter information depending on users{\textquoteright} profiles and suggest items that might match their preferences. While the majority of existing researches compute recommendation by considering only users and items, Context Aware Recommendation Systems (CARS) consider, in addition to users and items, others features related to the context. A first issue in CARS studies is to identify the contextual features. In this paper, we investigate the use of Text Classification techniques to extract contextual features from users{\textquoteright} reviews. We conduct experiments to identify the best classification algorithm for our dataset. We evaluate our approach on hotel reviews. We focus on extracting the trip type, as contextual information, from these reviews. Results show that the Multinomial Na{\"\i}ve Bayes performs best in our dataset, with a F1 score of 60.1\%. Since contextual information are not always provided in the reviews, we think that our results are promising. We conclude that this research area needs deeper studies. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/SITA.2013.6560804},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883074857\&doi=10.1109\%2fSITA.2013.6560804\&partnerID=40\&md5=440fb81da26cacbb428c326c7dc6cf50},
	author = {Lahlou, F.Z. and Mountassir, A. and Benbrahim, H. and Kassou, I.}
}
