Heterogeneous Hypergraph Embedding for Recommendation Systems
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917713011539968 |
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| author | Sakong, Darnbi Vu, Viet Hung Huynh, Thanh Trung Nguyen, Phi Le Yin, Hongzhi Nguyen, Quoc Viet Hung Nguyen, Thanh Tam |
| author_facet | Sakong, Darnbi Vu, Viet Hung Huynh, Thanh Trung Nguyen, Phi Le Yin, Hongzhi Nguyen, Quoc Viet Hung Nguyen, Thanh Tam |
| contents | Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. However, two main challenges persist: i) Neglecting complex higher-order interactions in the KG-based user-item network, potentially leading to sub-optimal recommendations, and ii) Dealing with the heterogeneous modalities of input sources, such as user-item bipartite graphs and KGs, which may introduce noise and inaccuracies. To address these issues, we present a novel Knowledge-enhanced Heterogeneous Hypergraph Recommender System (KHGRec). KHGRec captures group-wise characteristics of both the interaction network and the KG, modeling complex connections in the KG. Using a collaborative knowledge heterogeneous hypergraph (CKHG), it employs two hypergraph encoders to model group-wise interdependencies and ensure explainability. Additionally, it fuses signals from the input graphs with cross-view self-supervised learning and attention mechanisms. Extensive experiments on four real-world datasets show our model's superiority over various state-of-the-art baselines, with an average 5.18\% relative improvement. Additional tests on noise resilience, missing data, and cold-start problems demonstrate the robustness of our KHGRec framework. Our model and evaluation datasets are publicly available at \url{https://github.com/viethungvu1998/KHGRec}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Heterogeneous Hypergraph Embedding for Recommendation Systems Sakong, Darnbi Vu, Viet Hung Huynh, Thanh Trung Nguyen, Phi Le Yin, Hongzhi Nguyen, Quoc Viet Hung Nguyen, Thanh Tam Information Retrieval Artificial Intelligence Machine Learning Social and Information Networks Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. However, two main challenges persist: i) Neglecting complex higher-order interactions in the KG-based user-item network, potentially leading to sub-optimal recommendations, and ii) Dealing with the heterogeneous modalities of input sources, such as user-item bipartite graphs and KGs, which may introduce noise and inaccuracies. To address these issues, we present a novel Knowledge-enhanced Heterogeneous Hypergraph Recommender System (KHGRec). KHGRec captures group-wise characteristics of both the interaction network and the KG, modeling complex connections in the KG. Using a collaborative knowledge heterogeneous hypergraph (CKHG), it employs two hypergraph encoders to model group-wise interdependencies and ensure explainability. Additionally, it fuses signals from the input graphs with cross-view self-supervised learning and attention mechanisms. Extensive experiments on four real-world datasets show our model's superiority over various state-of-the-art baselines, with an average 5.18\% relative improvement. Additional tests on noise resilience, missing data, and cold-start problems demonstrate the robustness of our KHGRec framework. Our model and evaluation datasets are publicly available at \url{https://github.com/viethungvu1998/KHGRec}. |
| title | Heterogeneous Hypergraph Embedding for Recommendation Systems |
| topic | Information Retrieval Artificial Intelligence Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2407.03665 |