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Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation

Information Retrieval Journal volume 25, pages 44–90 (2022)Cite this article

Abstract

An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized ‘points-of-interest’ (POIs) to a user, if it can extract information from the user’s preference history (exploitation) and effectively blend it with the user’s current contextual information (exploration) to predict a POI’s ‘appropriateness’ in the current context. To balance this trade-off between exploitation and exploration, we propose an unsupervised, generic framework involving a factored relevance model (FRLM), constituting two distinct components, one pertaining to historical contexts, and the other corresponding to the current context. We further generalize the proposed FRLM by incorporating the semantic relationships between terms in POI descriptors using kernel density estimation (KDE) on embedded word vectors. Additionally, we show that trip-qualifiers, (e.g. ‘trip-type’, ‘accompanied-by’) are potentially useful information sources that could be used to improve the recommendation effectiveness. Using such information is not straightforward since users’ texts/reviews of visited POIs typically do not explicitly contain such annotations. We undertake a weakly supervised approach to predict the associations between the review-texts in a user profile and the likely trip contexts. Our experiments, conducted on the TREC Contextual Suggestion 2016 dataset, demonstrate that factorization, KDE-based generalizations, and trip-qualifier enriched contexts of the relevance model improve POI recommendation.

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Notes

  1. https://foursquare.com.

  2. https://tripadvisor.com.

  3. https://sites.google.com/site/treccontext/.

  4. https://www.kaggle.com/yelp-dataset/yelp-dataset.

  5. Available at https://www.inf.usi.ch/phd/aliannejadi/data.html.

  6. https://github.com/tmikolov/word2vec.

  7. Available at https://code.google.com/archive/p/word2vec/.

  8. Available at https://nlp.stanford.edu/projects/glove/.

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Acknowledgements

This work was supported by the ADAPT Centre for Digital Content Technology, funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

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Authors and Affiliations

  1. ADAPT Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

    Anirban Chakraborty

  2. School of Computing, University of Glasgow, Glasgow, UK

    Debasis Ganguly

  3. ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland

    Annalina Caputo & Gareth J. F. Jones

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  1. Anirban Chakraborty
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  2. Debasis Ganguly
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  3. Annalina Caputo
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  4. Gareth J. F. Jones
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Correspondence to Anirban Chakraborty.

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Chakraborty, A., Ganguly, D., Caputo, A. et al. Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation. Inf Retrieval J 25, 44–90 (2022). https://doi.org/10.1007/s10791-021-09400-9

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Keywords

  • Relevance model
  • Contextual recommendation
  • User preference model
  • Word-tag semantics
  • Word embedding
  • Kernel density estimation