A Context-Aware POI Recommendation

Tipajin Thaipisutikul, Ying Nong Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper proposes a novel sequential neural network that integrates the collaborative context-learning module to learn sparse and lengthy POI data for top-N recommendation tasks. Our proposed model explores either time and distance irregularities from check-in sequences and incorporates them into the model learning process by carrying a decomposition of the memory cell into short-and long-term interests. The short-term counterpart is adjusted by a proper weight and combined with the long-term counterpart before further processing with the light version of LSTM, where forget and input gates are united into a single decision unit. Also, we further improve our proposed model by allowing multiple contexts collaboratively trained together under the gating mechanism of recurrent neural networks. Extensive experiments over two public datasets show the superiority over existing baselines and state-of-the-art sequence-based models.

Original languageEnglish
Title of host publicationTENCON 2021 - 2021 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665495325
StatePublished - 2021
Event2021 IEEE Region 10 Conference, TENCON 2021 - Auckland, New Zealand
Duration: 7 Dec 202110 Dec 2021

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2021 IEEE Region 10 Conference, TENCON 2021
Country/TerritoryNew Zealand


  • deep-learning
  • machine learning
  • POI recommendation
  • spatio-temporal data


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