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• W2893346805 abstract "The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services. However, with the tremendous increase of users and POIs, POI recommender systems still face several challenging problems: (1) the hardness of modeling complex user-POI interactions from sparse implicit feedback; (2) the difficulty of incorporating the geographical context information. To cope with these challenges, we propose a novel autoencoder-based model to learn the complex user-POI relations, namely SAE-NAD, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD). In particular, unlike previous works equally treat users' checked-in POIs, our self-attentive encoder adaptively differentiates the user preference degrees in multiple aspects, by adopting a multi-dimensional attention mechanism. To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel. To evaluate the proposed model, we conduct extensive experiments on three real-world datasets with many state-of-the-art methods and evaluation metrics. The experimental results demonstrate the effectiveness of our model." @default.
• W2893346805 created "2018-10-05" @default.
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• W2893346805 date "2018-10-17" @default.
• W2893346805 modified "2023-10-15" @default.
• W2893346805 title "Point-of-Interest Recommendation" @default.
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• W2893346805 doi "https://doi.org/10.1145/3269206.3271733" @default.
• W2893346805 hasPublicationYear "2018" @default.
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