An Intelligent Recommender System Based on Association Rule Analysis for Requirement Engineering

Muhairat, Mohammad I.; AlZu’bi, Shadi; Hawashin, Bilal; Elbes, Mohammed; Al‐Ayyoub, Mahmoud

doi:10.3897/jucs.2020.003

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…Concluding the literature review, artificial intelligence (AI) and recent deep learning techniques could be applied to many areas that facilitate human lives [67,68]. Furthermore, it could be applied in medical applications [69,70], recommender systems [71], job-seeking [72], smart cities and localization [73], hospitals [74,75], object tracking [76][77][78], software engineering [79,80], E-commerce [81], emotional analysis [82], agriculture applications [83,84], and many others [85].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

et al. 2022

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Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

et al. 2022

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“…Many researchers have researched in the field of recommendation systems [22] [23], and there are many recommendations systems techniques that have been researched by researchers, and with the increase in information and the number of items that can be viewed or purchased, and the ability to analyze information, recommendation systems have become very important and necessary and there are a lot of recommendations techniques which have been investigated by many researchers. And with the increase in the number of items that users can buy/watch [25], recommendation systems [24] [28] have become necessary and available. Therefore, many researchers have directed their attention to recommendation systems and important applications in various fields such as music, e-commerce sites [30] [31], videos, e-learning [32], online review [19] and many others, as reviewed in [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Development: A Semantics-aware Trends for Movies Recommendation System using Modern NLP

AlZu’b¹,

Zraiqat²,

Hendawi³

2022

ijasca

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Recommendation systems are an important feature in the proposed virtual life, where users are often stuck with choices most of the time and need help to be able to find what they are looking for. In this work, contentbased techniques have been employed in the proposed recommender system in two ways, a deep review for content and features contents such as (cast, crew, keywords, and genres) has been conducted. A preprocessing stage using TF-IDF and CountVectorizer methods have been employed efficiently to prepare the dataset for any similarity measurements. Cosine similarity algorithm has been employed as well with and without sigmoid and linear kernals. The achieved result proves that similarities between movies using TF-IDF with - Cosine similarity (sigmoid kernel) overcomes the TF-IDF with - Cosine similarity (linear_kernel) and Cosine similarity with CountVectorizer in collaborative filtering. The accuracy values of different machine learning models are validated with K-fold Cross Validator techniques. The performance evaluation has been measured using ROOT Mean Square Error and Mean Absolute Error. Five Machine learning algorithms (NormalPredictor, SVD, KNNBasic (with k=20 and K=10), KNNBasic (with sim_options), and NMF (in several rating scales)). Accuracies are finally been validated with 3 folds from each validator. The best achieved RMSE and MAE scores are using SVD (RMSE = 90%) and (MAE = 69%), followed by KNNBasic (with sim_options, K= 20), NMF, KNNBasic (K=20), KNNBasic (K=10), ending with KNNBasic(sim_options, K= 10). Keywords: Recommendation System, Sustainable Development, Artificial Intelligence, Collaborative Filtering, Content-Based, Cosine Similarity, Movies Recommendation, NLP, Machine Learning Application.

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“…FP-Growth algorithm (derived from A-Priori) is an efficient algorithm for calculating frequently co-occurring items in a transaction database. FP-Growth algorithm is commonly applied in domains such as market basket analysis, but also has been applied to software engineering problems [Muhairat et al 2020]. To apply FP-Growth algorithm in the ASD domain, we consider the following:…”

Section: The Recommending Algorithmmentioning

confidence: 99%

Towards an ontology-based recommender system for agile practices adoption

Roldán

Bollati

Gonnet

2022

Anais Do XXV Congresso Ibero-Americano Em Engenharia De Software (CIbSE 2022)

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Choosing the right agile practices to be adopted in a software development process is complex, especially for organizations with little experience and a high turnover of human resources. To select the best practices, it is necessary to consider the characteristics that describe the team, the project, and the context in which they will be applied. In this paper, we propose an ontology-based agile practices recommender. The ontology was built and populated with knowledge obtained from software projects of Argentine organizations. The recommender applies the FP-Growth algorithm to find the most suitable practices for the target profile. In addition, it discovers potential conflicts between the profile and the recommended practices.

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An Intelligent Recommender System Based on Association Rule Analysis for Requirement Engineering

Cited by 13 publications

References 7 publications

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

Sustainable Development: A Semantics-aware Trends for Movies Recommendation System using Modern NLP

Towards an ontology-based recommender system for agile practices adoption

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