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RELINE: point-of-interest recommendations using multiple network embeddings

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Abstract

The rapid growth of users’ involvement in Location-Based Social Networks has led to the expeditious growth of the data on a global scale. The need of accessing and retrieving relevant information close to users’ preferences is an open problem which continuously raises new challenges for recommendation systems. The exploitation of points-of-interest (POIs) recommendation by existing models is inadequate due to the sparsity and the cold start problems. To overcome these problems many models were proposed in the literature; however, most of them ignore important factors, such as: geographical proximity, social influence, or temporal and preference dynamics, which tackle their accuracy while personalize their recommendations. In this work, we investigate these problems and present a unified model that jointly learns user’s and POI dynamics. Our proposal is termed RELINE (REcommendations with muLtIple Network Embeddings). More specifically, RELINE captures: (i) the social, (ii) the geographical, (iii) the temporal influence, and (iv) the users’ preference dynamics, by embedding eight relational graphs into one shared latent space. We have evaluated our approach against state-of-the-art methods with three large real-world datasets in terms of accuracy. Additionally, we have examined the effectiveness of our approach against the cold-start problem. Performance evaluation results demonstrate that significant performance improvement is achieved in comparison to existing state-of-the-art methods.

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Notes

  1. www.facebook.com/places.

  2. www.foursquare.com.

  3. www.yelp.com.

  4. https://sites.google.com/site/dbhongzhi.

  5. http://www.yongliu.org/datasets.

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Authors and Affiliations

  1. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Giannis Christoforidis, Pavlos Kefalas & Apostolos N. Papadopoulos

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

Authors
  1. Giannis Christoforidis
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  2. Pavlos Kefalas
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  3. Apostolos N. Papadopoulos
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  4. Yannis Manolopoulos
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Correspondence to Giannis Christoforidis.

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Christoforidis, G., Kefalas, P., Papadopoulos, A.N. et al. RELINE: point-of-interest recommendations using multiple network embeddings. Knowl Inf Syst 63, 791–817 (2021). https://doi.org/10.1007/s10115-020-01541-5

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