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- W2904362786 abstract "Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results." @default.
- W2904362786 created "2018-12-22" @default.
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- W2904362786 date "2019-07-17" @default.
- W2904362786 modified "2023-10-03" @default.
- W2904362786 title "HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation" @default.
- W2904362786 doi "https://doi.org/10.1609/aaai.v33i01.33013830" @default.
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