Improved Movie Recommendations Based on a Hybrid Feature Combination Method
Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing diffe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2019-08-01
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| Series: | Vietnam Journal of Computer Science |
| Subjects: | |
| Online Access: | http://www.worldscientific.com/doi/pdf/10.1142/S2196888819500192 |
| _version_ | 1818577967161278464 |
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| author | Gharbi Alshammari Stelios Kapetanakis Abdullah Alshammari Nikolaos Polatidis Miltos Petridis |
| author_facet | Gharbi Alshammari Stelios Kapetanakis Abdullah Alshammari Nikolaos Polatidis Miltos Petridis |
| author_sort | Gharbi Alshammari |
| collection | DOAJ |
| description | Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method. |
| first_indexed | 2024-12-16T06:38:19Z |
| format | Article |
| id | doaj.art-d01afc58534f499795c254e8c9d6b7df |
| institution | Directory Open Access Journal |
| issn | 2196-8888 2196-8896 |
| language | English |
| last_indexed | 2024-12-16T06:38:19Z |
| publishDate | 2019-08-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Vietnam Journal of Computer Science |
| spelling | doaj.art-d01afc58534f499795c254e8c9d6b7df2022-12-21T22:40:44ZengWorld Scientific PublishingVietnam Journal of Computer Science2196-88882196-88962019-08-016336337610.1142/S219688881950019210.1142/S2196888819500192Improved Movie Recommendations Based on a Hybrid Feature Combination MethodGharbi Alshammari0Stelios Kapetanakis1Abdullah Alshammari2Nikolaos Polatidis3Miltos Petridis4School of Computing, Engineering and Mathematics, University of Brighton, Lewes Road, Brighton BN2 4GJ, UKSchool of Computing, Engineering and Mathematics, University of Brighton, Lewes Road, Brighton BN2 4GJ, UKSchool of Computing, Engineering and Mathematics, University of Brighton, Lewes Road, Brighton BN2 4GJ, UKSchool of Computing, Engineering and Mathematics, University of Brighton, Lewes Road, Brighton BN2 4GJ, UKDepartment of Computer Science, Middlesex University, The Burroughs, Hendon, London NW4 4BT, UKRecommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method.http://www.worldscientific.com/doi/pdf/10.1142/S2196888819500192Recommender systemscollaborative filteringdemographic filteringhybrid recommendation |
| spellingShingle | Gharbi Alshammari Stelios Kapetanakis Abdullah Alshammari Nikolaos Polatidis Miltos Petridis Improved Movie Recommendations Based on a Hybrid Feature Combination Method Vietnam Journal of Computer Science Recommender systems collaborative filtering demographic filtering hybrid recommendation |
| title | Improved Movie Recommendations Based on a Hybrid Feature Combination Method |
| title_full | Improved Movie Recommendations Based on a Hybrid Feature Combination Method |
| title_fullStr | Improved Movie Recommendations Based on a Hybrid Feature Combination Method |
| title_full_unstemmed | Improved Movie Recommendations Based on a Hybrid Feature Combination Method |
| title_short | Improved Movie Recommendations Based on a Hybrid Feature Combination Method |
| title_sort | improved movie recommendations based on a hybrid feature combination method |
| topic | Recommender systems collaborative filtering demographic filtering hybrid recommendation |
| url | http://www.worldscientific.com/doi/pdf/10.1142/S2196888819500192 |
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