Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content Comparison
fIn the domain of recommendation systems, matrix decomposition is an effective strategy for mitigating issues related to sparsity and low space utilization. The Alternating Least Squares (ALS) method, in particular, stands out for its ability to process data in parallel, thereby enhancing computatio...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10840180/ |
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| author | Yongmao Yang Kampol Woradit Kenneth Cosh |
| author_facet | Yongmao Yang Kampol Woradit Kenneth Cosh |
| author_sort | Yongmao Yang |
| collection | DOAJ |
| description | fIn the domain of recommendation systems, matrix decomposition is an effective strategy for mitigating issues related to sparsity and low space utilization. The Alternating Least Squares (ALS) method, in particular, stands out for its ability to process data in parallel, thereby enhancing computational efficiency. However, when dealing with an original rating matrix, the ALS method may inadvertently sacrifice some information, leading to increased error rates. To address these challenges, this paper proposes a novel hybrid model that integrates matrix factorization with additional features. Furthermore, it leverages weighted similarity measures and employs advanced log-likelihood text mining techniques. These innovations are designed to tackle cold-start problems and sparsity issues while compensating for information loss to mitigate errors. Under the premise that our model employs consistent evaluation metrics and datasets, comparative analysis against existing models from related literature demonstrates superior performance. Specifically, our model achieves a lower Root Mean Square Error (RMSE) of 0.82 and 0.88, alongside a higher F1 score of 0.94 and 0.92 in two datasets. Our proposed hybrid approach effectively addresses sparsity and mitigates information loss in matrix factorization, as demonstrated by these results. |
| format | Article |
| id | doaj-art-d72c50d825c043fcba4d163e5d86379f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d72c50d825c043fcba4d163e5d86379f2025-01-24T00:01:22ZengIEEEIEEE Access2169-35362025-01-0113116091162210.1109/ACCESS.2025.352951510840180Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content ComparisonYongmao Yang0https://orcid.org/0009-0001-5181-6443Kampol Woradit1https://orcid.org/0000-0001-7383-0361Kenneth Cosh2Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, ThailandDepartment of Computer Engineering, Faculty of Engineering, Optimized AI Systems for Energy and Environmental Sustainability Research Group, Chiang Mai University, Chiang Mai, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, ThailandfIn the domain of recommendation systems, matrix decomposition is an effective strategy for mitigating issues related to sparsity and low space utilization. The Alternating Least Squares (ALS) method, in particular, stands out for its ability to process data in parallel, thereby enhancing computational efficiency. However, when dealing with an original rating matrix, the ALS method may inadvertently sacrifice some information, leading to increased error rates. To address these challenges, this paper proposes a novel hybrid model that integrates matrix factorization with additional features. Furthermore, it leverages weighted similarity measures and employs advanced log-likelihood text mining techniques. These innovations are designed to tackle cold-start problems and sparsity issues while compensating for information loss to mitigate errors. Under the premise that our model employs consistent evaluation metrics and datasets, comparative analysis against existing models from related literature demonstrates superior performance. Specifically, our model achieves a lower Root Mean Square Error (RMSE) of 0.82 and 0.88, alongside a higher F1 score of 0.94 and 0.92 in two datasets. Our proposed hybrid approach effectively addresses sparsity and mitigates information loss in matrix factorization, as demonstrated by these results.https://ieeexplore.ieee.org/document/10840180/Recommendation systemlog-likelihoodcontent-basedcollaborative filteringalternating least squaresparticle swarm optimization |
| spellingShingle | Yongmao Yang Kampol Woradit Kenneth Cosh Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content Comparison IEEE Access Recommendation system log-likelihood content-based collaborative filtering alternating least squares particle swarm optimization |
| title | Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content Comparison |
| title_full | Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content Comparison |
| title_fullStr | Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content Comparison |
| title_full_unstemmed | Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content Comparison |
| title_short | Hybrid Movie Recommendation System With User Partitioning and Log Likelihood Content Comparison |
| title_sort | hybrid movie recommendation system with user partitioning and log likelihood content comparison |
| topic | Recommendation system log-likelihood content-based collaborative filtering alternating least squares particle swarm optimization |
| url | https://ieeexplore.ieee.org/document/10840180/ |
| work_keys_str_mv | AT yongmaoyang hybridmovierecommendationsystemwithuserpartitioningandloglikelihoodcontentcomparison AT kampolworadit hybridmovierecommendationsystemwithuserpartitioningandloglikelihoodcontentcomparison AT kennethcosh hybridmovierecommendationsystemwithuserpartitioningandloglikelihoodcontentcomparison |