@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 {Lahlou20163312,
	title = {Improving recommendations using context from users{\textquoteright} reviews},
	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 = {3312-3315},
	abstract = {Recommender systems enable E-commerce websites to improve their sales by suggesting relevant items to customers. If considering the context of buying situations, more accurate recommendations can be computed. In this paper, we discuss the issue of using the contextual information contained in users{\textquoteright} reviews in order to improve recommendation. Copyright {\textcopyright} 2016 International Business Information Management Association},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984653667\&partnerID=40\&md5=a03c657d3c2b4702850bc5f2e897eb3a},
	author = {Lahlou, F.Z. and Benbrahim, H. and Kassou, I.}
}
@conference {Lahlou2013,
	title = {Context extraction from reviews for Context Aware Recommendation using Text Classification techniques},
	booktitle = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA},
	year = {2013},
	note = {cited By 0},
	abstract = {In this paper, we investigate the use of Text Classification techniques to extract contextual information from user reviews for Context Aware Recommendation. We conduct several experiments to identify the best Text Representation settings and the best classification algorithm for our dataset. We carry out our experiments on hotel reviews. We focus on extracting the trip type, as contextual information, from these reviews. Results show that the Na{\"\i}ve Bayes classifier yields the best results with up to 72.2\% in terms of F1-measure. To extract context from user reviews with text classification techniques, we recommend to use raw text rather than employing stemming, to use the normalized frequency based weighting rather than the presence based one, to remove terms that occur once in the data set, and to combine unigrams, bigrams and trigrams. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/AICCSA.2013.6616512},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887248381\&doi=10.1109\%2fAICCSA.2013.6616512\&partnerID=40\&md5=efcaf96161103c978184c9ad286a22cd},
	author = {Lahlou, F.Z. and Benbrahimand, H. and Mountassir, A. 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.}
}
