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A hybrid method of recommending POIs based on context and personal preference confidence

Published: 06 December 2016 Publication History

Abstract

It is a valuable study for Location-based Social Network (LBSN) make a more accurate Points-of-Interest (POI) recommendation since that can improve users' experiences. There have been many methods of POIs recommendation that consider context, personal preference pattern, and/or matrix factorization. However, the continuous contexts have not been thoroughly considered in these methods. This paper first proposes a locations splitting method which can handle both continuous and discrete contexts. Moreover, we present a Context-aware Probabilistic Matrix Factorization method (CPMF) that factorizes a frequency matrix of contexts and locations to obtain the user-location checkin probabilities. We design a Personal Preference Confidence (PPC) to extract a set of reliable POIs with confidence values for every user. Finally, we propose a hybrid recommender which fuses CPMF with PPC to recommend top-n POIs. Experiments on a large-scale real-world checkins dataset demonstrate that our recommendation method obtains a well performance and effect.

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cover image ACM Conferences
BDCAT '16: Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
December 2016
373 pages
ISBN:9781450346177
DOI:10.1145/3006299
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Published: 06 December 2016

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Author Tags

  1. continuous context
  2. hybrid recommender
  3. location splitting

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UCC '16
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