@article {Ajallouda2022440,
	title = {Automatic Key-Phrase Extraction: Empirical Study of Graph-Based Methods},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {440-456},
	abstract = {Key-phrases in a document are phrases that provide a high-level description of its content without reading it completely. In some research articles, authors specify key-phrases in the articles they have written. However, the vast majority of books, articles, and web pages published every day, lack key-phrases. The manual extraction of these phrases is a tedious task and takes a long time. For this reason, automatic key-phrase extraction (AKE), which is an area of Text Mining, remains the best solution to overcome these difficulties. Because they are used in many Natural Language Processing (NLP) applications, such as text summarization and text classification. This article presents a comparison of some methods of extracting key-phrases from documents. Especially the graph-based approaches. These approaches are evaluated by their abilities to extract key-phrases. Our work focuses on the study of the performance of these methods in extracting key-phrases, whether from short or long texts, with the aim of providing information that contributes to improving their efficiency. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_33},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135019633\&doi=10.1007\%2f978-3-031-07969-6_33\&partnerID=40\&md5=399dd65be1f33d58c24c1d31777d2b87},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Benlahmar, E.H.}
}
@conference {Ajallouda2022,
	title = {Doc2Vec, SBERT, InferSent, and USE Which embedding technique for noun phrases?},
	booktitle = {2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022},
	year = {2022},
	note = {cited By 4},
	abstract = {Phrase embedding is a technique of representing phrases in vector space. A very high effort has been made to develop this technique to improve tasking in various natural language processing (NLP) applications. The evaluation of phrase embedding has been presented in many studies, but most of them focused on the intrinsic or extrinsic evaluation process regardless of the type of the phrase (noun phrases, Verb phrases...). In the literature, there is no study evaluating the embedding of noun phrases, knowing that this type is used by many NLP applications, such as automatic key-phrase extraction (AKE), information retrieval, and question answering. In this article, we will present an empirical study to compare the most common phrase embedding techniques, to determine the most suitable for representing noun phrases. Dataset used in the comparison process consists of the noun phrases from the Inspec and SemEval2010 datasets, to which we have added their manually defined synonyms. {\textcopyright} 2022 IEEE.},
	keywords = {Embedding technique, Embeddings, Empirical studies, Key-phrases extractions, Natural language processing applications, Natural language processing systems, Noun phrase, Phrase embedding technique, Question Answering, Vector spaces},
	doi = {10.1109/IRASET52964.2022.9738300},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127985676\&doi=10.1109\%2fIRASET52964.2022.9738300\&partnerID=40\&md5=f274393a28334ba6d338a5dc4e3b5f79},
	author = {Ajallouda, L. and Najmani, K. and Zellou, A. and Benlahmar, E.H.}
}
@article {Najmani2022556,
	title = {The Impact of the k-Nearest Neighbor Parameters in Collaborative Filtering Recommender Systems},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {556-567},
	abstract = {Recommender Systems (RS) have become very important recently, they are a main component of many applications in different fields. They aim to give beneficial information according to the profile of each user among the huge existing online information. RS are based on several approaches to provide the best results and give satisfaction to the active user. Collaborative filtering is one of these approaches. It helps to choose a product according to the consumer{\textquoteright}s preference from many and various choices. It uses the k-Nearest Neighbor (kNN) technique for the extraction of similar users from the group of users. In this paper, we will study the effect of the parameters of the kNN algorithm on the obtained results. For that, we have varied the value of k, then we have measured for each value the prediction accuracy, using the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE) metrics. The experiments are carried out also to find the value of k which gives good results in both metrics. Then we have calculated also the RMSE and MAE metrics for different similarities in order to find the similarity which gives good results compared to others. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_42},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135066110\&doi=10.1007\%2f978-3-031-07969-6_42\&partnerID=40\&md5=c48c9d53d83b702abcc99c35254e6b72},
	author = {Najmani, K. and Ajallouda, L. and Benlahmar, E.H. and Sael, N. and Zellou, A.}
}
@conference {Najmani2022,
	title = {Offline and Online Evaluation for Recommender Systems},
	booktitle = {2022 International Conference on Intelligent Systems and Computer Vision, ISCV 2022},
	year = {2022},
	note = {cited By 0},
	abstract = {Recommender systems aim to facilitate decision making for users by offering them information according to their preferences, they are now popular in several application domains. The evaluation of recommender systems is very important to have an effective application in practice. In addition, it focuses to find better algorithms and evaluate their performance. However, researchers did not give much attention to it in this field. There are various ways to evaluate a recommender system. In this paper, we will discuss the main types of evaluations in this domain, which are offline and online evaluation, we will start with an overview of recommender systems, then we will present each type of evaluation, and we will compare the offline and the online evaluation for recommender systems. We will base on ten factors which are, the reproducibility, the reliability of the results of each type of evaluation, the preparation cost, the evaluation, the stability, the possibility of extensibility that{\textquoteright}s mean if we can add new metrics or not, the scalability, the passed time, deep analysis, and the sparsity metric. Finally, we will discuss the factors which are presented in the comparison. {\textcopyright} 2022 IEEE.},
	keywords = {Applications domains, Decision making, Decisions makings, It focus, Offline, Offline evaluation, On-line evaluation, Online systems, Performance, Recommender Systems, Reliability analysis, Reproducibilities},
	doi = {10.1109/ISCV54655.2022.9806059},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134293509\&doi=10.1109\%2fISCV54655.2022.9806059\&partnerID=40\&md5=31b11e740c58602df2b12bf3260a1ec9},
	author = {Najmani, K. and Ajallouda, L. and Benlahmar, E.H. and Sael, N. and Zellou, A.}
}
@article {Ajallouda202231,
	title = {A Systematic Literature Review of Keyphrases Extraction Approaches},
	journal = {International Journal of Interactive Mobile Technologies},
	volume = {16},
	number = {16},
	year = {2022},
	note = {cited By 0},
	pages = {31-58},
	abstract = {The keyphrases of a document are the textual units that characterize its content such as the topics it addresses, its ideas, their field, etc. Thousands of books, articles and web pages are published every day. Manually extracting keyphrases is a tedious task and takes a lot of time. Automatic keyphrases extraction is an area of text mining that aims to identify the most useful and important phrases that give meaning to the content of a document. Keyphrases can be used in many Natural Language Processing (NLP) applications, such as text summarization, text clustering and text classification. This article provides a Systematic Literature Review (SLR) to investigate, analyze, and discuss existing relevant contributions and efforts that use new concepts and tools to improve keyphrase extraction. We have studied the supervised and unsupervised approaches to extracting keyphrases published in the period 2015{\textendash}2022. We have also identified the steps most commonly used by the different approaches. Additionally, we looked at the criteria that should be evaluated to improve the accuracy of keyphrases extraction. Each selected approach was evaluated for its ability to extract keyphrases. Our findings highlight the importance of keyphrase extraction, and provide researchers and practitioners with information about proposed solutions and their limitations, which contributes to extract keyphrases in a powerful and meaningful way effective {\textcopyright} 2022, International Journal of Interactive Mobile Technologies.All Rights Reserved.},
	doi = {10.3991/ijim.v16i16.33081},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137632849\&doi=10.3991\%2fijim.v16i16.33081\&partnerID=40\&md5=cf158a54305193c3291a26b2c49d765f},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Benlahmar, E.H.}
}
@article {Ajallouda2022466,
	title = {Toward a New Process for Candidate Key-Phrases Extraction},
	journal = {Lecture Notes in Networks and Systems},
	volume = {455 LNNS},
	year = {2022},
	note = {cited By 3},
	pages = {466-474},
	abstract = {Key-phrases in a document are the terms that allow us to have or know an idea of its content without having to read it. They can be used in many Natural Language Processing (NLP) applications, such as text summarization, machine translation, and text classification. These phrases are selected from a set of terms in the document called candidate key-phrases. Thus, any flaws that may arise during the selection of candidate phrases may affect the automatic key-phrase extraction (AKE). Despite the importance of identifying candidate key-phrases in the AKE process, we found a very limited number of researchers interested in identifying their features in the document. In this paper, we will present the features that will allow the identification of candidate key-phrases, based on the study and analysis of the features of 60,000 key-phrases manually selected from five different datasets. To improve the performance of AKE approaches. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-02447-4_48},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130348254\&doi=10.1007\%2f978-3-031-02447-4_48\&partnerID=40\&md5=7bc1168842009b4c70c8fea6cc905514},
	author = {Ajallouda, L. and Hourrane, O. and Zellou, A. and Benlahmar, E.H.}
}
@conference {Ajallouda2020169,
	title = {K-means hac and fcm which clustering approach for arabic text?},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {169-174},
	abstract = {Today we are witnessing rapid growth in Web resources that allow Internet users to express and share their ideas opinions and judgments on a variety of issues. Several classification approaches have been proposed to classify textual data. But all these approaches require us to label the clusters we want to obtain. Which in reality is not available because we do not know in advance the information that can be proposed through these opinions. To overcome this constraint clustering approaches such as K-mean HAC or FCM can be exploited. In this paper we present and compare these approaches. And to show the importance of exploiting clustering algorithms to classify and analyze textual data in Arabic. By applying them to a real case that has created a great debate in Morocco which is the case of teachers contracting with academies. {\textcopyright} 2020 ACM.},
	keywords = {Arabic texts, Classification (of information), Classification approach, Clustering approach, Intelligent systems, Internet users, K-means clustering, Rapid growth, Real case, Textual data, Web resources},
	doi = {10.1145/3419604.3419779},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096415898\&doi=10.1145\%2f3419604.3419779\&partnerID=40\&md5=8aca383d8f485d15b3da191f03c5bf62},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Benlahmar, E.H.}
}
@article {Saif2017210,
	title = {GENAUM: New semantic distributed search engine},
	journal = {Journal of Mobile Multimedia},
	volume = {13},
	number = {3-4},
	year = {2017},
	note = {cited By 0},
	pages = {210-221},
	abstract = {The rapid development of services based on distributed architectures is now emerging as important items that transform mode of communication, and the exponential growth of the Web makes a strong pressure on technologies, for a regular improvement of performance, so it{\textquoteright}s irresistible to use distributed architectures and techniques for the search and information retrieval on the Web, to provide more relevant search result, in minimum possible time. This paper discuss some solutions researchers are working on, to make search engines more faster and more intelligent, specifically by considering the semantic context of users and documents, and the use of distributed architectures. This paper also presents the overall architecture of GENAUM; the collaborative, semantic and distributed search engine, based on a network of agents, which is the core part of the system. The functionality of GENAUM is spread across multiple agents, to fulfill user{\textquoteright}s performance expectations. At the end of this paper, some preliminary experimental results are presented, that attempts to test the user modeling process of GENAUM, using reference ontology. {\textcopyright} Rinton Press.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040242169\&partnerID=40\&md5=21e9c1f654b188cfb9b0bcd523769494},
	author = {Saif, I. and Doukkali, A.S. and Enaanai, A. and Benlahmar, E.H.}
}
