WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM
In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations...
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| Format: | Article |
| Language: | English |
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XLESCIENCE
2022-06-01
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| Series: | International Journal of Advances in Signal and Image Sciences |
| Subjects: | |
| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/96 |
| _version_ | 1818067211934236672 |
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| author | Mohamed Uvaze Ahmed Ayoobkhan Liayakath Ali Khan Subair Ali |
| author_facet | Mohamed Uvaze Ahmed Ayoobkhan Liayakath Ali Khan Subair Ali |
| author_sort | Mohamed Uvaze Ahmed Ayoobkhan |
| collection | DOAJ |
| description | In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%. |
| first_indexed | 2024-12-10T15:20:05Z |
| format | Article |
| id | doaj.art-13d1c43dee034f11b68044e12e364273 |
| institution | Directory Open Access Journal |
| issn | 2457-0370 |
| language | English |
| last_indexed | 2024-12-10T15:20:05Z |
| publishDate | 2022-06-01 |
| publisher | XLESCIENCE |
| record_format | Article |
| series | International Journal of Advances in Signal and Image Sciences |
| spelling | doaj.art-13d1c43dee034f11b68044e12e3642732022-12-22T01:43:43ZengXLESCIENCEInternational Journal of Advances in Signal and Image Sciences2457-03702022-06-018191610.29284/ijasis.8.1.2022.9-1696WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHMMohamed Uvaze Ahmed AyoobkhanLiayakath Ali Khan Subair AliIn this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%.https://xlescience.org/index.php/IJASIS/article/view/96personalized recommendation, user profiles, ontology, stemming algorithm, feature extraction and semantic knowledge. |
| spellingShingle | Mohamed Uvaze Ahmed Ayoobkhan Liayakath Ali Khan Subair Ali WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM International Journal of Advances in Signal and Image Sciences personalized recommendation, user profiles, ontology, stemming algorithm, feature extraction and semantic knowledge. |
| title | WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM |
| title_full | WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM |
| title_fullStr | WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM |
| title_full_unstemmed | WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM |
| title_short | WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM |
| title_sort | web page recommendation system by integrating ontology and stemming algorithm |
| topic | personalized recommendation, user profiles, ontology, stemming algorithm, feature extraction and semantic knowledge. |
| url | https://xlescience.org/index.php/IJASIS/article/view/96 |
| work_keys_str_mv | AT mohameduvazeahmedayoobkhan webpagerecommendationsystembyintegratingontologyandstemmingalgorithm AT liayakathalikhansubairali webpagerecommendationsystembyintegratingontologyandstemmingalgorithm |