Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions
Knowledge-enhanced recommendation (KER) aims to integrate the knowledge graph (KG) into collaborative filtering (CF) for alleviating the sparsity and cold start problems. The state-of-the-art graph neural network (GNN)–based methods mainly focus on exploiting the connectivity between entities in the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/17/2129 |
| _version_ | 1797521081434636288 |
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| author | Zhiqiang Pan Honghui Chen |
| author_facet | Zhiqiang Pan Honghui Chen |
| author_sort | Zhiqiang Pan |
| collection | DOAJ |
| description | Knowledge-enhanced recommendation (KER) aims to integrate the knowledge graph (KG) into collaborative filtering (CF) for alleviating the sparsity and cold start problems. The state-of-the-art graph neural network (GNN)–based methods mainly focus on exploiting the connectivity between entities in the knowledge graph, while neglecting the interaction relation between items reflected in the user-item interactions. Moreover, the widely adopted BPR loss for model optimization fails to provide sufficient supervisions for learning discriminative representation of users and items. To address these issues, we propose the collaborative knowledge-enhanced recommendation (CKER) method. Specifically, CKER proposes a collaborative graph convolution network (CGCN) to learn the user and item representations from the connection between items in the constructed interaction graph and the connectivity between entities in the knowledge graph. Moreover, we introduce the self-supervised learning to maximize the mutual information between the interaction- and knowledge-aware user preferences by deriving additional supervision signals. We conduct comprehensive experiments on two benchmark datasets, namely Amazon-Book and Last-FM, and the experimental results show that CKER can outperform the state-of-the-art baselines in terms of recall and NDCG on knowledge-enhanced recommendation. |
| first_indexed | 2024-03-10T08:06:38Z |
| format | Article |
| id | doaj.art-2b17eba9bc5e4b9c9bcd0d2733c43f9b |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T08:06:38Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2b17eba9bc5e4b9c9bcd0d2733c43f9b2023-11-22T10:58:21ZengMDPI AGMathematics2227-73902021-09-01917212910.3390/math9172129Collaborative Knowledge-Enhanced Recommendation with Self-SupervisionsZhiqiang Pan0Honghui Chen1Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, ChinaKnowledge-enhanced recommendation (KER) aims to integrate the knowledge graph (KG) into collaborative filtering (CF) for alleviating the sparsity and cold start problems. The state-of-the-art graph neural network (GNN)–based methods mainly focus on exploiting the connectivity between entities in the knowledge graph, while neglecting the interaction relation between items reflected in the user-item interactions. Moreover, the widely adopted BPR loss for model optimization fails to provide sufficient supervisions for learning discriminative representation of users and items. To address these issues, we propose the collaborative knowledge-enhanced recommendation (CKER) method. Specifically, CKER proposes a collaborative graph convolution network (CGCN) to learn the user and item representations from the connection between items in the constructed interaction graph and the connectivity between entities in the knowledge graph. Moreover, we introduce the self-supervised learning to maximize the mutual information between the interaction- and knowledge-aware user preferences by deriving additional supervision signals. We conduct comprehensive experiments on two benchmark datasets, namely Amazon-Book and Last-FM, and the experimental results show that CKER can outperform the state-of-the-art baselines in terms of recall and NDCG on knowledge-enhanced recommendation.https://www.mdpi.com/2227-7390/9/17/2129recommender systemsknowledge-enhanced recommendationcollaborative filteringknowledge graphself-supervised learning |
| spellingShingle | Zhiqiang Pan Honghui Chen Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions Mathematics recommender systems knowledge-enhanced recommendation collaborative filtering knowledge graph self-supervised learning |
| title | Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions |
| title_full | Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions |
| title_fullStr | Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions |
| title_full_unstemmed | Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions |
| title_short | Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions |
| title_sort | collaborative knowledge enhanced recommendation with self supervisions |
| topic | recommender systems knowledge-enhanced recommendation collaborative filtering knowledge graph self-supervised learning |
| url | https://www.mdpi.com/2227-7390/9/17/2129 |
| work_keys_str_mv | AT zhiqiangpan collaborativeknowledgeenhancedrecommendationwithselfsupervisions AT honghuichen collaborativeknowledgeenhancedrecommendationwithselfsupervisions |