@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.}
}
