Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier
Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/10/1/6 |
| _version_ | 1797494910461411328 |
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| author | Korab Rrmoku Besnik Selimi Lule Ahmedi |
| author_facet | Korab Rrmoku Besnik Selimi Lule Ahmedi |
| author_sort | Korab Rrmoku |
| collection | DOAJ |
| description | Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy. |
| first_indexed | 2024-03-10T01:41:01Z |
| format | Article |
| id | doaj.art-19fc7b57e15241d8a6186d6e31455918 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-10T01:41:01Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-19fc7b57e15241d8a6186d6e314559182023-11-23T13:23:16ZengMDPI AGComputation2079-31972022-01-01101610.3390/computation10010006Application of Trust in Recommender Systems—Utilizing Naive Bayes ClassifierKorab Rrmoku0Besnik Selimi1Lule Ahmedi2Faculty of Contemporary Sciences and Technologies, South East European University, 1200 Tetovo, North MacedoniaFaculty of Contemporary Sciences and Technologies, South East European University, 1200 Tetovo, North MacedoniaFaculty of Electrical and Computer Engineering, University of Prishtina, 10000 Prishtina, KosovoReceiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy.https://www.mdpi.com/2079-3197/10/1/6Naïve Bayes algorithmprobabilistic Bayesian classifiersrecommender systemssocial networkstrustworthinessclassification methods |
| spellingShingle | Korab Rrmoku Besnik Selimi Lule Ahmedi Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier Computation Naïve Bayes algorithm probabilistic Bayesian classifiers recommender systems social networks trustworthiness classification methods |
| title | Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier |
| title_full | Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier |
| title_fullStr | Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier |
| title_full_unstemmed | Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier |
| title_short | Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier |
| title_sort | application of trust in recommender systems utilizing naive bayes classifier |
| topic | Naïve Bayes algorithm probabilistic Bayesian classifiers recommender systems social networks trustworthiness classification methods |
| url | https://www.mdpi.com/2079-3197/10/1/6 |
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