A hybrid semantic recommender system based on an improved clustering

A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users according to their (previous) interests. These systems are expected to recommend the items according to the users’ interests. There are two trad...

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Veröffentlicht in:	The Journal of supercomputing Jg. 80; H. 9; S. 13341 - 13385
Hauptverfasser:	Bahrani, Payam, Minaei-Bidgoli, Behrouz, Parvin, Hamid, Mirzarezaee, Mitra, Keshavarz, Ahmad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2024 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Approximation Clustering Cold Collaboration Compilers Computer Science Feedback Filtration Interpreters Machine learning Ontology Processor Architectures Programming Languages Ratings Recommender systems Semantics Sparsity Recommendation systems Kmeans clustering nearest neighbors Ontology WordNet
ISSN:	0920-8542, 1573-0484
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Abstract	A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users according to their (previous) interests. These systems are expected to recommend the items according to the users’ interests. There are two traditional general recommender system models, i.e., Collaborative Filtering Recommender System (ColFRS) and Content-based Filtering Recommender System (ConFRS). Also, there is another model that is a hybrid of those two traditional recommender systems; it is called Hybrid Recommender System (HRS). An HRS usually outperforms simple traditional recommender systems. The problems such as scalability, cold start, and sparsity belong to the main problems that any recommender system should solve. The memory-based (modeless) recommender systems benefit from good accuracies. But they suffer from a lack of admissible scalability. The model-based recommender systems suffer from a lack of admissible accuracies. But they benefit from good scalability. In this paper, it is tried to propose a hybrid model based on an automatically improved ontology to solve the scalability, cold start, and sparsity problems. Our proposed HRS also uses an innovative approach of clustering as an augmented section. When there are enough ratings, it uses a collaborative filtering approach to predict the missing ratings. When there are not enough ratings, it uses a content-based filtering approach to predict the missing ratings. In the content-based filtering section of our proposed HRS, ontology concepts are used to improve the accuracy of ratings’ prediction. If our target client is severely sparse, we cannot trust even the ratings predicted by the content-based filtering section of our proposed HRS. Therefore, our proposed HRS uses additive clustering to improve the prediction of the missing ratings if the target client is severely sparse. It is experimentally shown that our model outperforms many of the newly developed recommender systems.
AbstractList	A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users according to their (previous) interests. These systems are expected to recommend the items according to the users’ interests. There are two traditional general recommender system models, i.e., Collaborative Filtering Recommender System (ColFRS) and Content-based Filtering Recommender System (ConFRS). Also, there is another model that is a hybrid of those two traditional recommender systems; it is called Hybrid Recommender System (HRS). An HRS usually outperforms simple traditional recommender systems. The problems such as scalability, cold start, and sparsity belong to the main problems that any recommender system should solve. The memory-based (modeless) recommender systems benefit from good accuracies. But they suffer from a lack of admissible scalability. The model-based recommender systems suffer from a lack of admissible accuracies. But they benefit from good scalability. In this paper, it is tried to propose a hybrid model based on an automatically improved ontology to solve the scalability, cold start, and sparsity problems. Our proposed HRS also uses an innovative approach of clustering as an augmented section. When there are enough ratings, it uses a collaborative filtering approach to predict the missing ratings. When there are not enough ratings, it uses a content-based filtering approach to predict the missing ratings. In the content-based filtering section of our proposed HRS, ontology concepts are used to improve the accuracy of ratings’ prediction. If our target client is severely sparse, we cannot trust even the ratings predicted by the content-based filtering section of our proposed HRS. Therefore, our proposed HRS uses additive clustering to improve the prediction of the missing ratings if the target client is severely sparse. It is experimentally shown that our model outperforms many of the newly developed recommender systems.
Author	Bahrani, Payam Mirzarezaee, Mitra Keshavarz, Ahmad Parvin, Hamid Minaei-Bidgoli, Behrouz
Author_xml	– sequence: 1 givenname: Payam surname: Bahrani fullname: Bahrani, Payam organization: Department of Computer Engineering, Science and Research Branch, Islamic Azad University – sequence: 2 givenname: Behrouz surname: Minaei-Bidgoli fullname: Minaei-Bidgoli, Behrouz organization: School of Computer Engineering, Iran University of Science and Technology – sequence: 3 givenname: Hamid surname: Parvin fullname: Parvin, Hamid email: parvin@iust.ac.ir organization: Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University – sequence: 4 givenname: Mitra surname: Mirzarezaee fullname: Mirzarezaee, Mitra organization: Department of Computer Engineering, Science and Research Branch, Islamic Azad University – sequence: 5 givenname: Ahmad surname: Keshavarz fullname: Keshavarz, Ahmad organization: Department of Electrical Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University
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Snippet	A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users...
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SubjectTerms	Accuracy Algorithms Approximation Clustering Cold Collaboration Compilers Computer Science Feedback Filtration Interpreters Machine learning Ontology Processor Architectures Programming Languages Ratings Recommender systems Semantics Sparsity
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Title	A hybrid semantic recommender system based on an improved clustering
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