Hybrid image recommendation algorithm combining content and collaborative filtering approaches
The paper relates to the subject field of image recommender systems. The proposed approach of automatic image recommendation addresses the following shortcomings of existing solutions: manual input of metadata by users, the lack of user rating history consideration, significant computational resourc...
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| Published in | Procedia computer science Vol. 193; pp. 200 - 209 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1877-0509 1877-0509 |
| DOI | 10.1016/j.procs.2021.10.020 |
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| Abstract | The paper relates to the subject field of image recommender systems. The proposed approach of automatic image recommendation addresses the following shortcomings of existing solutions: manual input of metadata by users, the lack of user rating history consideration, significant computational resources. The main idea of the proposed approach is to recognize object classes from images using a convolutional neural network to make recommendations. In the proposed solution users and images are located in the semantic space represented by a graph. Software implementation of the proposed approach and obtained results are considered. |
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| AbstractList | The paper relates to the subject field of image recommender systems. The proposed approach of automatic image recommendation addresses the following shortcomings of existing solutions: manual input of metadata by users, the lack of user rating history consideration, significant computational resources. The main idea of the proposed approach is to recognize object classes from images using a convolutional neural network to make recommendations. In the proposed solution users and images are located in the semantic space represented by a graph. Software implementation of the proposed approach and obtained results are considered. |
| Author | Kobyshev, Kirill Nikiforov, Igor Voinov, Nikita |
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| Keywords | collaborative filtering image recommender system graph database word2vec convolutional neural network content filtering |
| Language | English |
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| References_xml | – reference: Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. (2015). “Going Deeper with Convolutions.“ Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015: 1-9. – reference: Ramzi Karam. 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| SubjectTerms | collaborative filtering content filtering convolutional neural network graph database image recommender system word2vec |
| Title | Hybrid image recommendation algorithm combining content and collaborative filtering approaches |
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