Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs
Recommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Alth...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10041 |
| _version_ | 1827727408594157568 |
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| author | Haemin Jung Heesung Park Kwangyon Lee |
| author_facet | Haemin Jung Heesung Park Kwangyon Lee |
| author_sort | Haemin Jung |
| collection | DOAJ |
| description | Recommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Although recent deep learning-based recommendation algorithms that embed KGs have demonstrated impressive performance, the richness of semantics and explainability embedded in the KGs are often lost due to the opaque nature of vector representations in deep neural networks. To address this issue, we propose a novel user profiling method for recommender systems that can encapsulate user preferences while preserving the original semantics of the KGs, using frequent subgraph mining. Our approach involves creating user profile vectors from a set of frequent subgraphs that contain information about user preferences and the strength of those preferences, measured by frequency. Subsequently, we trained a deep neural network model to learn the relationship between users and items, thereby facilitating effective recommendations using the neural network’s approximation ability. We evaluated our user profiling methodology on movie data and found that it demonstrated competitive performance, indicating that our approach can accurately represent user preferences while maintaining the semantics of the KGs. This work, therefore, presents a significant step towards creating more transparent and effective recommender systems that can be beneficial for a wide range of applications and readers interested in this field. |
| first_indexed | 2024-03-10T23:05:59Z |
| format | Article |
| id | doaj.art-f31b112dc5244cc196bc1fd81a89adb1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:05:59Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f31b112dc5244cc196bc1fd81a89adb12023-11-19T09:22:11ZengMDPI AGApplied Sciences2076-34172023-09-0113181004110.3390/app131810041Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge GraphsHaemin Jung0Heesung Park1Kwangyon Lee2Department of Industrial Engineering, Yonsei University, Seodaemun-gu, Seoul 03722, Republic of KoreaDepartment of Industrial Engineering, Yonsei University, Seodaemun-gu, Seoul 03722, Republic of KoreaSchool of Electronic Engineering, Soongsil University, Dongjak-gu, Seoul 06978, Republic of KoreaRecommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Although recent deep learning-based recommendation algorithms that embed KGs have demonstrated impressive performance, the richness of semantics and explainability embedded in the KGs are often lost due to the opaque nature of vector representations in deep neural networks. To address this issue, we propose a novel user profiling method for recommender systems that can encapsulate user preferences while preserving the original semantics of the KGs, using frequent subgraph mining. Our approach involves creating user profile vectors from a set of frequent subgraphs that contain information about user preferences and the strength of those preferences, measured by frequency. Subsequently, we trained a deep neural network model to learn the relationship between users and items, thereby facilitating effective recommendations using the neural network’s approximation ability. We evaluated our user profiling methodology on movie data and found that it demonstrated competitive performance, indicating that our approach can accurately represent user preferences while maintaining the semantics of the KGs. This work, therefore, presents a significant step towards creating more transparent and effective recommender systems that can be beneficial for a wide range of applications and readers interested in this field.https://www.mdpi.com/2076-3417/13/18/10041frequent subgraph miningknowledge graphrecommender systemuser profiling |
| spellingShingle | Haemin Jung Heesung Park Kwangyon Lee Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs Applied Sciences frequent subgraph mining knowledge graph recommender system user profiling |
| title | Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs |
| title_full | Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs |
| title_fullStr | Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs |
| title_full_unstemmed | Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs |
| title_short | Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs |
| title_sort | enhancing recommender systems with semantic user profiling through frequent subgraph mining on knowledge graphs |
| topic | frequent subgraph mining knowledge graph recommender system user profiling |
| url | https://www.mdpi.com/2076-3417/13/18/10041 |
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