Online book recommendation system by using collaborative filtering and association mining

Parvatikar, Suhasini; Joshi, Bharti

doi:10.1109/iccic.2015.7435717

Cited by 30 publications

(7 citation statements)

References 5 publications

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“…Hui Chen et al [22], introduced a recommendation model to specify the behavior of e-learners and proposed an adaptive model based on learner behavior. Parvatikar et al [23] have adapted different approaches for suggesting books to purchase. They primarily according to the book-based collaborative filtering apply data mining schemes.…”

Section: Related Workmentioning

confidence: 99%

DHM-Ocr

Mekala,

TNS,

Rao Tandu

2024

Int. j. electr. comput. eng. syst. (Online)

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In recent years, there has been an increase in online education resources to help learners improve their skills. However, it is difficult to select the right course from available online education resources due to the demands and needs of learners with different knowledge domains. To solve this problem, an online course recommendation model has the important factor of enhancing learner's knowledge. Many existing recommendation systems (RS) use collaborative filtering (CF) to recommend courses to learners. The major problems with the Collaborative Filtering Recommendation System (CFRS) are the sparse preferences and the scalability of the data. According to the similarity of items, many recommendation models are proposed and developed, but none of these provide suggestions to users without their associations or preferences. We propose a deep hybrid model-online course recommendation (DHM-OCR) that uses high-level learner behavior and course objective features. We demonstrate the improvements and efficiency of the model for suggesting online e-learning courses. According to the analysis and evaluation results, it seems that our DHM-OCR outperforms the parallel research recommendation system. Experimental findings from online course data reveal that the suggested model and approach significantly improve classification accuracy and training efficiency, particularly limited available data.

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Section: Related Workmentioning

confidence: 99%

DHM-Ocr

Mekala,

TNS,

Rao Tandu

2024

Int. j. electr. comput. eng. syst. (Online)

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“…Collaborative filtering is a very common technique for book recommendation [18] [19] [20]. But the accuracy of this technique was 88% [21] or 89% [22], which is comparatively low. However, a content-based recommendation system needs an enormous amount of training data set, which is not feasible for real-world scenarios [2].…”

Section: Background and Related Workmentioning

confidence: 99%

Personalized Book Recommendation System using Machine Learning Algorithm

Sarma¹,

Mittra²,

Hossain³

2021

IJACSA

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As the amounts of online books are exponentially increasing due to COVID-19 pandemic, finding relevant books from a vast e-book space becomes a tremendous challenge for online users. Personal recommendation systems have been emerged to conduct effective search which mine related books based on user rating and interest. Most of these existing systems are user-based ratings where content-based and collaborativebased learning methods are used. These systems' irrationality is their rating technique, which counts the users who have already been unsubscribed from the services and no longer rate books. This paper proposed an effective system for recommending books for online users that rated a book using the clustering method and then found a similarity of that book to suggest a new book. The proposed system used the K-means Cosine Distance function to measure distance and Cosine Similarity function to find Similarity between the book clusters. Sensitivity, Specificity, and F Score were calculated for ten different datasets. The average Specificity was higher than sensitivity, which means that the classifier could re-move boring books from the reader's list. Besides, a receiver operating characteristic curve was plotted to find a graphical view of the classifiers' accuracy. Most of the datasets were close to the ideal diagonal classifier line and far from the worst classifier line. The result concludes that recommendations, based on a particular book, are more accurately effective than a user-based recommendation system.

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“…The recommendation systems aim to suggest items to users. The recommendation systems directly advise users to items that can meet their needs and desires by narrowing down information in large database [8].…”

Section: A Recommendation Systemmentioning

confidence: 99%

Decision Support System of Lecturer Selection Recommendation with Collaborative Filtering Method

Honggowibowo¹,

Wintolo²,

Indrianingsih³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The use of decision support systems for the selection of the best lecturers at Adisutjipto Institute of Technology (STTA) is not yet at the level of application. The average tertiary institution in selecting the best lecturers uses certain criteria, for example the criteria in teaching, research and community service. Of the three indicators of lecturer performance in Indonesia, we only use two indicators, namely research and community service. From these two indicators, we made five criteria, namely the number of presenters at the conference, the amount of community service, the number of unpublished research, the number of published research, and the number of citations of scientific articles. The selection was made by users, namely STTA Director, Vice Director for Academic Affairs, Vice Director for Financial Affairs and Community Service Research Center to 62 lecturers who were active in research, community service and publications indexed on Google Scholar. This user restriction is adjusted to the organizational structure at our university, where the five users have authority in assessing the performance of lecturers and giving awards to lecturers who are declared as the best lecturers. After Collaborative Filtering method is done to predict the final result using the rating given by the user. Based on the results of the system testing, it was concluded that the system that was built could be a solution to help select lecturers who were eligible to be given awards as the best of lecturer in the field of research and community service.

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Online book recommendation system by using collaborative filtering and association mining

Cited by 30 publications

References 5 publications

DHM-Ocr

DHM-Ocr

Personalized Book Recommendation System using Machine Learning Algorithm

Decision Support System of Lecturer Selection Recommendation with Collaborative Filtering Method

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