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Semantic Annotation for Places in LBSN through Graph Embedding

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Published:06 November 2017Publication History

ABSTRACT

With the prevalence of location-based social networks (LBSNs), automated semantic annotation for places plays a critical role in many LBSN-related applications. Although a line of research continues to enhance labeling accuracy, there is still a lot of room for improvement. The crucial problem is to find a high-quality representation for each place. In previous works, the representation is usually derived directly from observed patterns of places or indirectly from calculated proximity amongst places or their combination. In this paper, we also exploit the combination to represent places but present a novel semi-supervised learning framework based on graph embedding, called Predictive Place Embedding (PPE). For place proximity, PPE first learns user embeddings from a user-tag bipartite graph by minimizing supervised loss in order to preserve the similarity of users visiting analogous places. User similarity is then transformed into place proximity by optimizing each place embedding as the centroid of the vectors of its check-in users. Our underlying idea is that a place can be considered as a representative of all its visitors. For observed patterns, a place-temporal bipartite graph is used to further adjust place embeddings by reducing unsupervised loss. Extensive experiments on real large LBSNs show that PPE outperforms state-of-the-art methods significantly.

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      cover image ACM Conferences
      CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
      November 2017
      2604 pages
      ISBN:9781450349185
      DOI:10.1145/3132847

      Copyright © 2017 ACM

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      Publication History

      • Published: 6 November 2017

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      CIKM '17 Paper Acceptance Rate171of855submissions,20%Overall Acceptance Rate1,861of8,427submissions,22%

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