Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis
Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, has shown substantial promise in collecting sequential patterns to improve movie recommendations among the di...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10292849/ |
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| author | Zulfiqar Ali Asif Muhammad Ahmad Sami Al-Shamayleh Kashif Naseer Qureshi Wagdi Alrawagfeh Adnan Akhunzada |
| author_facet | Zulfiqar Ali Asif Muhammad Ahmad Sami Al-Shamayleh Kashif Naseer Qureshi Wagdi Alrawagfeh Adnan Akhunzada |
| author_sort | Zulfiqar Ali |
| collection | DOAJ |
| description | Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, has shown substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra-metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter-metapath analysis. The intra-metapath analysis investigates interactions within a single metapath, whereas the inter-metapath analysis investigates links between numerous metapaths. Intra and inter-metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user’s interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter-metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things. |
| first_indexed | 2024-03-08T19:37:36Z |
| format | Article |
| id | doaj.art-bcd672151d9d47f9a44489e176175c85 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:37:36Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bcd672151d9d47f9a44489e176175c852023-12-26T00:06:22ZengIEEEIEEE Access2169-35362023-01-011111901711903210.1109/ACCESS.2023.332727110292849Enhancing Performance of Movie Recommendations Using LSTM With Meta Path AnalysisZulfiqar Ali0https://orcid.org/0000-0002-0613-3280Asif Muhammad1https://orcid.org/0000-0001-9215-4100Ahmad Sami Al-Shamayleh2https://orcid.org/0000-0002-7222-2433Kashif Naseer Qureshi3https://orcid.org/0000-0003-3045-8402Wagdi Alrawagfeh4https://orcid.org/0000-0003-4227-9276Adnan Akhunzada5https://orcid.org/0000-0001-8370-9290Department of Computer Science, COMSATS University Islamabad, Islamabad, PakistanFAST School of Computing, National University of Computer and Emerging Sciences, Islamabad, PakistanDepartment of Data Science and Artificial Intelligence, Al-Ahliyya Amman University, Amman, JordanDepartment of Electronic and Computer Engineering, University of Limerick, Limerick, IrelandCollege of Computing and IT, University of Doha for Science and Technology, Doha, QatarCollege of Computing and IT, University of Doha for Science and Technology, Doha, QatarMovie recommendation algorithms play an important role in assisting consumers in identifying films that match their likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, has shown substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra-metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter-metapath analysis. The intra-metapath analysis investigates interactions within a single metapath, whereas the inter-metapath analysis investigates links between numerous metapaths. Intra and inter-metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user’s interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter-metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things.https://ieeexplore.ieee.org/document/10292849/Long short-term memoryinter-metapathintra-metapathmetapath analysismetapath instancesablation analysis |
| spellingShingle | Zulfiqar Ali Asif Muhammad Ahmad Sami Al-Shamayleh Kashif Naseer Qureshi Wagdi Alrawagfeh Adnan Akhunzada Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis IEEE Access Long short-term memory inter-metapath intra-metapath metapath analysis metapath instances ablation analysis |
| title | Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis |
| title_full | Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis |
| title_fullStr | Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis |
| title_full_unstemmed | Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis |
| title_short | Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis |
| title_sort | enhancing performance of movie recommendations using lstm with meta path analysis |
| topic | Long short-term memory inter-metapath intra-metapath metapath analysis metapath instances ablation analysis |
| url | https://ieeexplore.ieee.org/document/10292849/ |
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