A Collaborative Filtering Approach Based on Naïve Bayes Classifier
Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8787761/ |
| _version_ | 1818945327491710976 |
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| author | Priscila Valdiviezo-Diaz Fernando Ortega Eduardo Cobos Raul Lara-Cabrera |
| author_facet | Priscila Valdiviezo-Diaz Fernando Ortega Eduardo Cobos Raul Lara-Cabrera |
| author_sort | Priscila Valdiviezo-Diaz |
| collection | DOAJ |
| description | Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable recommendations; and model-based, whose recommendations are more precise but hard to understand. Here we propose a Bayesian model that not only provides us with recommendations as good as matrix factorization models, but these predictions can also be explained. The model is based on both user-based and item-based collaborative filtering approaches, which recommends items by using similar users' and items' information, respectively. Experiments carried out using four datasets present good results compared to several state-of-the-art baselines, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also improving the prediction's accuracy in some datasets. |
| first_indexed | 2024-12-20T07:57:21Z |
| format | Article |
| id | doaj.art-01986723c9d24da49384aca1bb81de32 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T07:57:21Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-01986723c9d24da49384aca1bb81de322022-12-21T19:47:37ZengIEEEIEEE Access2169-35362019-01-01710858110859210.1109/ACCESS.2019.29330488787761A Collaborative Filtering Approach Based on Naïve Bayes ClassifierPriscila Valdiviezo-Diaz0https://orcid.org/0000-0002-5216-8820Fernando Ortega1https://orcid.org/0000-0003-4765-1479Eduardo Cobos2Raul Lara-Cabrera3https://orcid.org/0000-0002-7959-1936Computer Science and Electronic Department, Universidad Técnica Particular de Loja, Loja, EcuadorDepartamento de Lenguajes y Sistemas Informáticos, ETSI de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, SpainIngenio Labs, Madrid, SpainDepartamento de Lenguajes y Sistemas Informáticos, ETSI de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, SpainRecommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable recommendations; and model-based, whose recommendations are more precise but hard to understand. Here we propose a Bayesian model that not only provides us with recommendations as good as matrix factorization models, but these predictions can also be explained. The model is based on both user-based and item-based collaborative filtering approaches, which recommends items by using similar users' and items' information, respectively. Experiments carried out using four datasets present good results compared to several state-of-the-art baselines, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also improving the prediction's accuracy in some datasets.https://ieeexplore.ieee.org/document/8787761/Recommender systemscollaborative filteringNaïve Bayes classifierhybrid CFreliability measure |
| spellingShingle | Priscila Valdiviezo-Diaz Fernando Ortega Eduardo Cobos Raul Lara-Cabrera A Collaborative Filtering Approach Based on Naïve Bayes Classifier IEEE Access Recommender systems collaborative filtering Naïve Bayes classifier hybrid CF reliability measure |
| title | A Collaborative Filtering Approach Based on Naïve Bayes Classifier |
| title_full | A Collaborative Filtering Approach Based on Naïve Bayes Classifier |
| title_fullStr | A Collaborative Filtering Approach Based on Naïve Bayes Classifier |
| title_full_unstemmed | A Collaborative Filtering Approach Based on Naïve Bayes Classifier |
| title_short | A Collaborative Filtering Approach Based on Naïve Bayes Classifier |
| title_sort | collaborative filtering approach based on na x00ef ve bayes classifier |
| topic | Recommender systems collaborative filtering Naïve Bayes classifier hybrid CF reliability measure |
| url | https://ieeexplore.ieee.org/document/8787761/ |
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