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#6324. Variational Bayesian representation learning for grocery recommendation
| September 2026 | publication date |
| Proposal available till | 20-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Library and Information Sciences; Information Systems; |
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| 2350 $ | 1200 $ | 1050 $ | 900 $ |
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Representation learning has been widely applied in real-world recommendation systems to capture the features of both users and items. Existing grocery recommendation methods only represent each user and item by single deterministic points in a low-dimensional continuous space, which limit the expressive ability of their embeddings, resulting in recommendation performance bottlenecks. In addition, existing representation learning methods for grocery recommendation only consider the items (products) as independent entities, neglecting their other valuable side information, such as the textual descriptions and the categorical data of items. In this paper, we propose the Variational Bayesian Context-Aware Representation (VBCAR) model for grocery recommendation. VBCAR is a novel variational Bayesian model that learns distributional representations of users and items by leveraging basket context information from historical interactions. Our VBCAR model is also extendable to leverage side information by encoding contextual features into representations based on the inference encoder. We conduct extensive experiments on three real-world grocery datasets to assess the effectiveness of our model as well as the impact of different construction strategies for item side information.
Keywords:
Grocery recommendation; Representation learning; Side information; Variational Bayesian
Contacts :
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Representation learning has been widely applied in real-world recommendation systems to capture the features of both users and items. Existing grocery recommendation methods only represent each user and item by single deterministic points in a low-dimensional continuous space, which limit the expressive ability of their embeddings, resulting in recommendation performance bottlenecks. In addition, existing representation learning methods for grocery recommendation only consider the items (products) as independent entities, neglecting their other valuable side information, such as the textual descriptions and the categorical data of items. In this paper, we propose the Variational Bayesian Context-Aware Representation (VBCAR) model for grocery recommendation. VBCAR is a novel variational Bayesian model that learns distributional representations of users and items by leveraging basket context information from historical interactions. Our VBCAR model is also extendable to leverage side information by encoding contextual features into representations based on the inference encoder. We conduct extensive experiments on three real-world grocery datasets to assess the effectiveness of our model as well as the impact of different construction strategies for item side information.
Keywords:
Grocery recommendation; Representation learning; Side information; Variational Bayesian
Contacts :
