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• W3202545208 abstract "Popularity of Location-based Social Networks (LBSNs) provide opportunity to collect massive multi modal datasets that contain geographical information, as well as time and social interactions. Such data is a useful resource for generating personalized location recommendations. Such heterogeneous data can be further extended with notions of trust between users, popularity of locations and expertise of users. Recently the use Heterogeneous Information Network (HIN) models and graph neural architectures have proven successful for recommendation problem. One limitation of such solution is on capturing the contextual relationships between the nodes in the heterogeneous network. In location recommendation, spatial context is a frequently used consideration such that users prefer to get recommendations within their spatial vicinity. To solve this challenging problem, we propose a novel Heterogeneous Information Network (HIN) embedding technique, SgWalk, which explores the proximity between users and locations and generates location recommendations via subgraph-based node embedding. SgWalk follows four steps: building users subgraphs according to location context, generating random walk sequences over user subgraphs, learning embeddings of nodes in LBSN graph, and generating location recommendations using vector representation of the nodes. SgWalk is differentiated from existing techniques relying on meta-path or bi-partite graphs by means of utilizing the contextual user subgraph. By this way, it is aimed to capture contextual relationships among heterogeneous nodes more effectively. The recommendation accuracy of SgWalk is analyzed through extensive experiments conducted on benchmark datasets in terms of top-n location recommendations. The accuracy evaluation results indicate minimum 23% (@5 recommendation) average improvement in accuracy compared to baseline techniques and the state-of-the-art heterogeneous graph embedding techniques in the literature." @default.
• W3202545208 created "2021-10-11" @default.
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• W3202545208 date "2021-01-01" @default.
• W3202545208 modified "2023-10-17" @default.
• W3202545208 title "SgWalk: Location Recommendation by User Subgraph-Based Graph Embedding" @default.
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• W3202545208 doi "https://doi.org/10.1109/access.2021.3116226" @default.
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