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

Anirban Chakraborty, Debasis Ganguly, Annalina Caputo, Gareth J. F. Jones

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)44-90
Number of pages47
JournalInformation Retrieval
Volume25
Issue number1
Early online date21 Jan 2022
DOIs
Publication statusPublished - 1 Mar 2022

Keywords

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

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